{"as_of":"2026-08-07T18:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8bb6ed84505fd54ed838d22d1e97367cf4a53f0e105289827f7e4944668d282a","coverage":[{"denominator":88,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":88,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T11:45:59.210161Z","state":"measured"},{"denominator":88,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":88,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.22488/citation-record","integrity":"/paper/2507.22488/integrity","json":"/paper/2507.22488/citation-record.json","paper":"/paper/2507.22488"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:58.794350Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.794350Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:3f8199327307999d2e52f3fe070d4a762bcaceb5cf9f73e6331bc536353b27f7","observation_id":"422c5dee-27f4-4e1d-9003-0e70a570cd86","resolution":{"observed_at":"2026-08-06T11:45:58.794350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:58.798890Z","title":"Federated learning for privacy- preserving ai,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.798890Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:ddc2b4f3c66b6bbd00c8edc5801365a395253d25bf9b204c9399e98b2f7b6f21","observation_id":"281543f7-ce4d-4bec-8c4b-4b2113b8d22d","resolution":{"observed_at":"2026-08-06T11:45:58.798890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:58.803005Z","title":"Federated machine learning: Concept and applications,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.803005Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:f9eec4716693ed4b69eea48c061e0c080fb7c01e5cae0170ab0ba5acee67eaf8","observation_id":"094b823d-405b-44ea-aa7d-479ad873ceba","resolution":{"observed_at":"2026-08-06T11:45:58.803005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.12814","last_updated":"2023-09-28T01:29:46Z","snapshot_observed_at":"2026-07-06T14:22:04.764657Z","submitted_at":"2022-11-23T10:00:06Z","title":"Vertical Federated Learning: Concepts, Advances and Challenges","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.12814","snapshot_observed_at":"2026-08-06T11:45:58.807763Z","title":"Vertical federated learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.807763Z"},"links":{"cited_paper":"/paper/2211.12814","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:4fd901b9425728022b940829f2761301ea6b76917bd10352ef5f7671a1b561fd","observation_id":"9e4da73f-9bf0-40c9-8da5-9c4cb997bf7e","resolution":{"observed_at":"2026-08-06T11:45:58.807763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:58.812731Z","title":"Federated learning in mobile edge networks: A comprehensive survey,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.812731Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:c12b2aa0246b0cfeeddfba6b91214f08a3fb56b2a19d7b82a77ae56845cc300e","observation_id":"c9b6fb58-1d2f-4c83-b88a-fa07b4d80963","resolution":{"observed_at":"2026-08-06T11:45:58.812731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.16270","last_updated":"2023-03-30T00:42:31Z","snapshot_observed_at":"2026-07-06T15:09:08.648296Z","submitted_at":"2023-03-28T19:30:23Z","title":"Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.16270","snapshot_observed_at":"2026-08-06T11:45:58.816502Z","title":"Communication-efficient vertical federated learning with limited overlapping samples,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.816502Z"},"links":{"cited_paper":"/paper/2303.16270","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:508612cb3598235b0ccf09c2579189937821e16a4f2c1e8480c0361abdcb6c84","observation_id":"c037080a-fd27-44f7-ab5f-f0a18611a71e","resolution":{"observed_at":"2026-08-06T11:45:58.816502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:58.820702Z","title":"Fedcvt: Semi-supervised vertical federated learning with cross-view training,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.820702Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:4deed082aa5a426154bbb0c61a5c74ecfa04c74e2cd14ee287bbad7f4bc92014","observation_id":"733d0272-7c7c-40be-8293-2da4b44a3b74","resolution":{"observed_at":"2026-08-06T11:45:58.820702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.04309","last_updated":"2024-08-05T12:58:37Z","snapshot_observed_at":"2026-07-06T12:35:56.858445Z","submitted_at":"2022-02-09T06:56:41Z","title":"Vertical Federated Learning: Challenges, Methodologies and Experiments","version":2},"cited_work":{"arxiv_id":"2202.04309","doi":null,"metadata_source":"pith","pith_arxiv_id":"2202.04309","snapshot_observed_at":"2026-08-06T11:46:00.209310Z","title":"Vertical Federated Learning: Challenges, Methodologies and Experiments","venue":"cs.LG","work_id":"7b482082-e806-4ec8-acb0-b08d0ca69f79","year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.824795Z"},"links":{"cited_paper":"/paper/2202.04309","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:68527b43b4ab4ff14bcfd6b6b461bfd1190f4dffe4d2ef4e6f26b76c0de014d7","observation_id":"c86e6f2b-9c3d-4ac7-9ba4-89b39406ea34","resolution":{"observed_at":"2026-08-06T11:46:00.275809Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:58.828602Z","title":"Semi-supervised federated heterogeneous transfer learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.828602Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:52ed7161177333ac2908865a2f0002b82191ba239be0113cf14917bf8c4678af","observation_id":"24f4df2a-6237-488e-8aad-f978fc1c8f81","resolution":{"observed_at":"2026-08-06T11:45:58.828602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.08934","last_updated":"2023-06-08T10:05:43Z","snapshot_observed_at":"2026-07-06T13:43:13.386359Z","submitted_at":"2022-08-18T16:15:15Z","title":"A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning","version":2},"cited_work":{"arxiv_id":"2208.08934","doi":null,"metadata_source":"pith","pith_arxiv_id":"2208.08934","snapshot_observed_at":"2026-08-06T11:45:59.912820Z","title":"A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning","venue":"cs.LG","work_id":"29fd7c6b-8f31-4852-8b9f-36af2548a1a1","year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.832411Z"},"links":{"cited_paper":"/paper/2208.08934","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:4e952e46bfa7e390a4150cb64c9b064436a5cc3d8e9cf4158646863c85cc96f1","observation_id":"4f8e1d83-22db-42a6-8836-c8c805e381b1","resolution":{"observed_at":"2026-08-06T11:46:00.052989Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.11896","last_updated":"2021-02-18T02:23:50Z","snapshot_observed_at":"2026-08-03T17:49:43.841065Z","submitted_at":"2021-01-28T09:57:30Z","title":"Self-supervised Cross-silo Federated Neural Architecture Search","version":2},"cited_work":{"arxiv_id":"2101.11896","doi":null,"metadata_source":"pith","pith_arxiv_id":"2101.11896","snapshot_observed_at":"2026-08-06T11:45:59.679083Z","title":"Self-supervised Cross-silo Federated Neural Architecture Search","venue":"cs.LG","work_id":"9f1d233e-eba6-4344-8b77-7ee50848c4eb","year":2021},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.836682Z"},"links":{"cited_paper":"/paper/2101.11896","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:d14ec9b1c2c22e7d0b2d251c909a18eb4acc15dfe25c8af21cc6c01e90d74bd3","observation_id":"48408368-0131-4342-8c62-a10bfbc7e27f","resolution":{"observed_at":"2026-08-06T11:45:59.766579Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:58.840915Z","title":"Self-supervised vertical feder- ated learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.840915Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:c8b7c087eacbb9b8b09e8c144e4beab02bcff2f953fb80e79025b4de69777f4e","observation_id":"1babcd15-d9ed-487e-b75e-dbef93618381","resolution":{"observed_at":"2026-08-06T11:45:58.840915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:58.845009Z","title":"Vertical semi- federated learning for efficient online advertising,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.845009Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:841b7e8df7ddd222f9682956bffedcdfaf5f7484bda39f784e8617863f575527","observation_id":"2f1d9969-731a-4cf6-8ec4-f5b102697006","resolution":{"observed_at":"2026-08-06T11:45:58.845009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:58.848072Z","title":"Multi-view federated learning with data collaboration,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.848072Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:94a50c05674bd200beaae4cfd6602e77070f77c2acfb9e6b25cb8e8c7b84c579","observation_id":"2307e8f4-8691-4774-9302-748fcb666a7e","resolution":{"observed_at":"2026-08-06T11:45:58.848072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:01.051729Z","title":"Vertical federated learning-based feature selection with non- overlapping sample utilization,","venue":null,"work_id":"87d8be71-f4b5-492d-9f3b-e6d4a5449f84","year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.851532Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:27b38377d1cf1f57a4fcd0747a9395057d2753a092d1fd734a9c53a3b5c3dbc8","observation_id":"00ce1846-c076-413d-a302-aa5147b19065","resolution":{"observed_at":"2026-08-06T11:46:01.054855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:01.042404Z","title":"Practical vertical federated learning with unsupervised representation learning,","venue":null,"work_id":"5f40d2e6-600a-4872-bc10-0838929a551e","year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.856522Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:0f3b129c7619920b712e44118bbaec98f677c89a367f1b532cf9f5c3d9990e7a","observation_id":"8770ab97-bf49-428b-9499-f70cb9b2e92f","resolution":{"observed_at":"2026-08-06T11:46:01.045513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:01.031925Z","title":"A review of the oversampling techniques in class imbalance problem,","venue":null,"work_id":"90d46a9c-00d1-460e-894b-087993574793","year":2021},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.859966Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:7009a34aba08a7dee8de4b05858e02ccbb04c659bd967d4934d0144c42fc32f3","observation_id":"68cefc2f-12e2-4976-9f86-6dc16350d738","resolution":{"observed_at":"2026-08-06T11:46:01.035480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:01.022012Z","title":"A review on imbalanced data handling using undersampling and oversampling technique,","venue":null,"work_id":"5fea1e41-998f-4efb-b029-b629423b0f7e","year":2017},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.863382Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:b16a98c61eda6a308086316e90ba04765edaa46f99d2851b93e1919c21f45ef0","observation_id":"84a1e2d4-2d4e-48d4-b02a-57acb5f03ce5","resolution":{"observed_at":"2026-08-06T11:46:01.025094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08300","last_updated":"2020-06-23T02:12:29Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-22T22:28:47Z","title":"Overcoming Noisy and Irrelevant Data in Federated Learning","version":2},"cited_work":{"arxiv_id":"2001.08300","doi":null,"metadata_source":"pith","pith_arxiv_id":"2001.08300","snapshot_observed_at":"2026-08-06T11:45:59.417975Z","title":"Overcoming Noisy and Irrelevant Data in Federated Learning","venue":"cs.LG","work_id":"2f035ecb-ff0d-4f25-b7d7-d4d7f915bdbc","year":2020},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.867102Z"},"links":{"cited_paper":"/paper/2001.08300","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:a1942008b8689b828c1eef5e1449ca1c2777bd431695a48e5ad7dcb19debb04f","observation_id":"a3ee2bd1-dc5f-4cb9-a0c2-74686f8a28dd","resolution":{"observed_at":"2026-08-06T11:45:59.538352Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10619","last_updated":"2020-07-19T21:02:14Z","snapshot_observed_at":"2026-07-06T08:59:49.011788Z","submitted_at":"2020-02-25T01:36:43Z","title":"Three Approaches for Personalization with Applications to Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10619","snapshot_observed_at":"2026-08-06T11:45:58.871376Z","title":"Three approaches for personalization with applications to federated learning,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.871376Z"},"links":{"cited_paper":"/paper/2002.10619","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:76e16766924d57a81772b92fdc320778ea7122fbfbb11351a3933c1a1673a2fb","observation_id":"294525c7-8d89-4c6a-b1f8-b641b253d10d","resolution":{"observed_at":"2026-08-06T11:45:58.871376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:01.012051Z","title":"Attribute-based classifi- cation for zero-shot visual object categorization,","venue":null,"work_id":"117c5742-3c19-47d7-9475-0ed1aeeceb1d","year":2013},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.875700Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:d12cedbe434ca44552d816dd2cd5b791db68b75ed7eb5a07ce3af4dbded0f5e2","observation_id":"95d3a178-ae30-414c-bb40-c21edd52b231","resolution":{"observed_at":"2026-08-06T11:46:01.015340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:01.002065Z","title":"Zero-data learning of new tasks","venue":null,"work_id":"2889915b-cfad-4035-9178-82bd73f7ba9e","year":2008},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.879609Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:588ba28935a39fb35449fc6eabaa15fdcc6bf4262ec6ec45d339f4728b09a35e","observation_id":"ce3ccc6b-68e3-4686-9d74-5d66f16b402a","resolution":{"observed_at":"2026-08-06T11:46:01.005230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.992122Z","title":"Learning hypergraph-regularized attribute predictors,","venue":null,"work_id":"ef7f0a7d-40d5-477d-8634-374462d475e6","year":2015},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.883221Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:3580e34a46871fe260e7ea64b54d714474861ff4456ed48395cc71a8996d993a","observation_id":"f2c68f2c-a0b6-4ee4-88a6-24c89e97d36a","resolution":{"observed_at":"2026-08-06T11:46:00.995658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.981078Z","title":"Learning multimodal latent attributes,","venue":null,"work_id":"71b22a8e-1149-4155-935c-d94131de1d23","year":2014},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.886689Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:7329e3c33a74f6d7901cf51529bd739b78a285c99cfae23fa5a2f950fc38bbb6","observation_id":"56b87a91-c277-4faa-b49d-8373b01ee3eb","resolution":{"observed_at":"2026-08-06T11:46:00.984563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.971584Z","title":"Zero-shot recognition with unreliable attributes,","venue":null,"work_id":"e7ddd32f-7526-41a9-b3ce-3f27dce65377","year":2024},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.890506Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:5916cf51a6cbe6a9ae1f7ba5566f5e9e627ce242eefe2acb9246bbb48fdf7e68","observation_id":"6626cc98-1d2f-4439-93b2-5d3e3864f8a7","resolution":{"observed_at":"2026-08-06T11:46:00.974443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.951285Z","title":"Attribute-based classifi- cation for zero-shot visual object categorization,","venue":null,"work_id":"19e8fd1d-137e-4ccc-8454-8c1543541faa","year":2014},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.898293Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:dce5dc5d16d598e9f8e52b087fb1616f2d5e2d2ac88c03d352823e03079ba0d9","observation_id":"b23733fb-7208-40d6-97f8-22ce5334dadf","resolution":{"observed_at":"2026-08-06T11:46:00.954977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.940143Z","title":"Generative zero-shot learning via low- rank embedded semantic dictionary,","venue":null,"work_id":"e85a698a-ae0f-4ec7-9c1b-948893dc30d3","year":2019},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.901908Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:9aefa88a72dffd9bae8b4180f52e2a7b1525eb1a9aff768a76b42e87acfee8da","observation_id":"88719f2c-22e2-4386-8213-4f01eed3d471","resolution":{"observed_at":"2026-08-06T11:46:00.944657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.928616Z","title":"Zero-shot learning via latent space encoding,","venue":null,"work_id":"fb24e7a1-029f-4137-8e2b-f3e1330616b3","year":2019},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.905110Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:ba3574b08b4891098738d9b90acf0ebdc1bf01fb201236b47b9ac5c7fcd74aae","observation_id":"29f32135-ae8f-4824-a039-743acacc3210","resolution":{"observed_at":"2026-08-06T11:46:00.932657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.916887Z","title":"Transduc- tive zero-shot learning with a self-training dictionary approach,","venue":null,"work_id":"4672c7f5-50e3-42cc-b7a5-190a96bd3a8b","year":2018},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.908449Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:259ce777fa22c39934b03b39b2b1e75e84f344ba8ec84dc8bdd33ff0f331ffc4","observation_id":"c5690373-c836-4ac2-96cd-ffcf823c5e25","resolution":{"observed_at":"2026-08-06T11:46:00.920295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00981","last_updated":"2018-04-12T12:17:47Z","snapshot_observed_at":"2026-07-06T06:12:32.936457Z","submitted_at":"2017-12-04T10:00:40Z","title":"Feature Generating Networks for Zero-Shot Learning","version":2},"cited_work":{"arxiv_id":"1712.00981","doi":null,"metadata_source":"pith","pith_arxiv_id":"1712.00981","snapshot_observed_at":"2026-08-06T11:45:59.370289Z","title":"Feature Generating Networks for Zero-Shot Learning","venue":"cs.CV","work_id":"57c7781e-6c8a-4b06-a692-e1c53615e955","year":2017},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.912054Z"},"links":{"cited_paper":"/paper/1712.00981","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:2decb34fcea93d1167c11d0c2f9c63a32589f4bd354a5a38689f06261791e89c","observation_id":"12b28cb3-8e3e-4b2c-adf1-f2fcc38c220f","resolution":{"observed_at":"2026-08-06T11:45:59.382184Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.906298Z","title":"General- ized zero- and few-shot learning via aligned variational autoencoders,","venue":null,"work_id":"f5fa701c-a23a-4153-a193-831e7d89c90a","year":2019},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.915575Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:90e226041d9147194f0b2b34cb5b73494acc1013d65601806427c23ca8f66fb9","observation_id":"a34c8a0b-5668-4f24-9b9d-54ba5fb38237","resolution":{"observed_at":"2026-08-06T11:46:00.909701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.895786Z","title":"Fedproto: Federated prototype learning across heterogeneous clients,","venue":null,"work_id":"df081a25-ba85-4825-bb96-b347eefdfa96","year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.918636Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:64a18e2bf1a2465278defe3cb927449cca1d778f85991a4ba2cbaccec0e70211","observation_id":"028886ef-a747-457e-803a-28d5f34c7387","resolution":{"observed_at":"2026-08-06T11:46:00.899359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.886241Z","title":"Fedfa: Federated learning with feature anchors to align features and classifiers for heterogeneous data,","venue":null,"work_id":"6fe680c6-6134-4b78-b589-79b655a2c175","year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.922440Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:e756c587d6f4dd831d88e718a46ecaa3282aa1c1264b651d10f0bbaa6e87fde2","observation_id":"1e4f6cb6-9d30-41da-8566-bd1db8723822","resolution":{"observed_at":"2026-08-06T11:46:00.889706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11867","last_updated":"2023-06-20T19:58:58Z","snapshot_observed_at":"2026-08-06T13:05:33.985582Z","submitted_at":"2023-06-20T19:58:58Z","title":"Personalized Federated Learning with Feature Alignment and Classifier Collaboration","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11867","snapshot_observed_at":"2026-08-06T11:45:58.925905Z","title":"Personalized federated learning with feature alignment and classifier collaboration,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.925905Z"},"links":{"cited_paper":"/paper/2306.11867","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:13fa3fc4a214cf2fbf982142376a68bfdbbcebc4cff876d49d1e88cc8b88f12d","observation_id":"e0e4ccfc-75a6-4a18-9d6d-eb3a86e5ef0d","resolution":{"observed_at":"2026-08-06T11:45:58.925905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.876084Z","title":"Tackling data heterogeneity in federated learning with class prototypes,","venue":null,"work_id":"d4f45dbd-263a-4a80-b3a5-44083bbc433e","year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.930807Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:44f9a391db793051244c329131431786e4c60fc50a9e9c9e11bae3c508d4094a","observation_id":"7512860c-5ca1-4e9e-8683-e19d47f936d4","resolution":{"observed_at":"2026-08-06T11:46:00.879518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:58.934167Z","title":"Fedproc: Prototypical contrastive federated learning on non-iid data,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.934167Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:0e87a40e344c86a9b892d47c25b3cf95d25873ef0a00cd72e666fe58af27510e","observation_id":"7d2bf1fb-8626-4dfa-9bb5-7a83744a9013","resolution":{"observed_at":"2026-08-06T11:45:58.934167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.860184Z","title":"Contrastive-enhanced domain generalization with federated learning,","venue":null,"work_id":"b26604c8-8a18-4cff-b903-0e9419b2f21b","year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.937189Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:d6e23b62042a7a6c253221454a6b82b0a4c81675b0e995ed9fb56b69d9387c6c","observation_id":"56672f37-b571-483f-ae45-e1bf670bd333","resolution":{"observed_at":"2026-08-06T11:46:00.863718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.850755Z","title":"Vertical federated knowledge trans- fer via representation distillation for healthcare collaboration networks,","venue":null,"work_id":"7eacddd4-b697-4abd-aa27-4ce96f40fb75","year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.940698Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:302b6e500ccd19b0b0f4872a01012565b711e1b823fbb3a4d114d174a173fa2d","observation_id":"4e20427e-9249-4e11-bbc8-20f7168b1de3","resolution":{"observed_at":"2026-08-06T11:46:00.854083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.841797Z","title":"Improving availability of vertical federated learning: Relaxing inference on non-overlapping data,","venue":null,"work_id":"e1acdcf6-53c3-4b5a-955f-48e69e402b1c","year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.943987Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:e4ecfc3d88f2687b8f7681360b0b1248d03ae08f639f7c96b9abe05a58e0d2e8","observation_id":"078a5084-47b1-4647-aee6-dc9261be3e85","resolution":{"observed_at":"2026-08-06T11:46:00.844718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.04035","last_updated":"2018-03-20T21:46:12Z","snapshot_observed_at":"2026-07-06T06:27:42.036363Z","submitted_at":"2018-03-11T20:53:18Z","title":"Entity Resolution and Federated Learning get a Federated Resolution","version":2},"cited_work":{"arxiv_id":"1803.04035","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.04035","snapshot_observed_at":"2026-08-06T11:45:59.325553Z","title":"Entity Resolution and Federated Learning get a Federated Resolution","venue":"cs.DB","work_id":"45a7713d-f69e-420e-871a-d81e65eaadc1","year":2018},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.947223Z"},"links":{"cited_paper":"/paper/1803.04035","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:63a4656027e111c622b24b5c17a9ac7c37b16e629e4956054684c40de477c18f","observation_id":"be3f8c53-9628-4b4c-ba89-56cac966b8e4","resolution":{"observed_at":"2026-08-06T11:45:59.336513Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.10677","last_updated":"2017-11-29T04:29:29Z","snapshot_observed_at":"2026-07-06T06:11:44.900667Z","submitted_at":"2017-11-29T04:29:29Z","title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.10677","snapshot_observed_at":"2026-08-06T11:45:58.950694Z","title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.950694Z"},"links":{"cited_paper":"/paper/1711.10677","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:abaed612e94c3b26ba319abb6e614f104226b40f476e28eb52f6a59014152ced","observation_id":"047fdd2e-f266-4baf-b8db-394023011103","resolution":{"observed_at":"2026-08-06T11:45:58.950694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.830915Z","title":"Opti- mal transport based one-shot federated learning for artificial intelligence of things,","venue":null,"work_id":"da6a3caf-1f8c-4857-85b0-5bf32a9c0455","year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.954326Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:54724525c77777eea185bdfd8c0e2e52f9544b46f2613b7406ecc2a89ff901ac","observation_id":"7be908fa-58c8-4f05-a5eb-7df8b756a83a","resolution":{"observed_at":"2026-08-06T11:46:00.833898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.821939Z","title":"Global and local prompts coop- eration via optimal transport for federated learning,","venue":null,"work_id":"edf950b2-d021-431e-aae6-43d107515578","year":2024},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.957674Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:d19993eec32a67eab511fdd1cec2cad3f4f14792f83ebf2d527866f041afeda8","observation_id":"5905b1b4-ec77-40b7-8c3d-ebd24334b6fc","resolution":{"observed_at":"2026-08-06T11:46:00.825143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.812422Z","title":"Spectr: Fast speculative decoding via optimal transport,","venue":null,"work_id":"99c253de-a251-4530-bace-3bb0bf09c402","year":2024},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.961207Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:49be3802ea6b9197339888620a25cdd596231fee524c50e523d6f99b30b49a5c","observation_id":"69a7c1d5-49cb-4d73-8500-e927d8f9bb4c","resolution":{"observed_at":"2026-08-06T11:46:00.815545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.803228Z","title":"Bayes’ theorem,","venue":null,"work_id":"734d5919-4364-40c5-9e49-6ff60ff888db","year":2003},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.964982Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:c561eca66f9dcba0cd0e74ee4f73da56a13cec7a22eae48435c735dae814387b","observation_id":"eb848160-65db-491a-b872-923da3cd13cc","resolution":{"observed_at":"2026-08-06T11:46:00.806347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.10288","last_updated":"2017-11-28T13:39:10Z","snapshot_observed_at":"2026-07-06T06:11:35.990430Z","submitted_at":"2017-11-28T13:39:10Z","title":"Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.10288","snapshot_observed_at":"2026-08-06T11:45:58.968465Z","title":"Minimal-entropy correlation alignment for unsupervised deep domain adaptation,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.968465Z"},"links":{"cited_paper":"/paper/1711.10288","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:1b2a79f884688d4e1de243ef31ace3d5e592c035da92b26ea3fc0cff755b3ed5","observation_id":"9125c0be-3310-4f42-854e-c7b0f917bbbc","resolution":{"observed_at":"2026-08-06T11:45:58.968465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.01690","last_updated":"2020-02-05T09:13:19Z","snapshot_observed_at":"2026-07-06T08:55:00.847404Z","submitted_at":"2020-02-05T09:13:19Z","title":"Entropy Minimization vs. Diversity Maximization for Domain Adaptation","version":1},"cited_work":{"arxiv_id":"2002.01690","doi":null,"metadata_source":"pith","pith_arxiv_id":"2002.01690","snapshot_observed_at":"2026-08-06T11:45:59.264674Z","title":"Entropy Minimization vs. Diversity Maximization for Domain Adaptation","venue":"cs.LG","work_id":"25920b00-155a-4e8b-95d0-b164821b0724","year":2020},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.972623Z"},"links":{"cited_paper":"/paper/2002.01690","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:99b1d04ae3ccd2867caa7f1235c9434e0e623f9b41896e980816452a5f24c4a6","observation_id":"da953bc6-5be2-4a06-a35a-50eab2e385b0","resolution":{"observed_at":"2026-08-06T11:45:59.286075Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.792741Z","title":"von liebig’s law of the minimum and plankton ecology (1899–1991),","venue":null,"work_id":"4ead63ab-d023-44d6-8d96-2cdfeabb6d83","year":1991},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.977138Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:4d98b1bab6e3c1370fc03887c495ccb5193532e33e36678556aeb437e2ca56f1","observation_id":"454f8b5c-fc58-415e-8d99-abd022a4fd1e","resolution":{"observed_at":"2026-08-06T11:46:00.795918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.782860Z","title":"Enhancing supervised learning with unlabeled data,","venue":null,"work_id":"2992255f-3175-4b29-b7e3-ddc5e4308f4c","year":2000},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.981297Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:b4afac88216c34610d698df952179666b5a06b6a6eb03060dfa6ba2aff90fc91","observation_id":"0926fba9-0aa8-40d0-834a-88250c07d6d7","resolution":{"observed_at":"2026-08-06T11:46:00.786160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.772496Z","title":"Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure,","venue":null,"work_id":"e390c998-973f-44bd-b2a5-6767c858a803","year":2002},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.078476Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:b83fe644ee706ad5c008e67b41685aece5ea6e2c13cf0d43d7eeff4ab6ef90fe","observation_id":"f89f11f6-6188-44cc-8fe3-02ad37a6553e","resolution":{"observed_at":"2026-08-06T11:46:00.776028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.762795Z","title":"A unified solution for privacy and communication efficiency in vertical federated learning,","venue":null,"work_id":"50622bb9-e226-4782-8a10-796ecfeef69b","year":2024},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.083448Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:433fc589f84382111f0bdbfa0df7eb227acdb613c9ff4c36bd144041ca6ed65d","observation_id":"e34ba576-d6b6-4477-86bb-a9b1ffb64dc8","resolution":{"observed_at":"2026-08-06T11:46:00.765928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.752903Z","title":"Flexible vertical federated learning with heterogeneous parties,","venue":null,"work_id":"f5899149-6d88-46c9-b29c-29d5c3a794af","year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.087756Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:ca5343cdaac9856bc58283d3df8ecba8b3377c91b400acb40d1bceff9ebbac18","observation_id":"89519635-eeb9-489e-96fa-97594ca69e8f","resolution":{"observed_at":"2026-08-06T11:46:00.756524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.742584Z","title":"Twenty years of mixture of experts,","venue":null,"work_id":"87fae87d-22ef-4754-beca-8d9c0b8541a2","year":2012},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.091675Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:b23fb1c0f1de48b563e9120ac8fb521fce41281f6ab29830c98546ffd5bee30e","observation_id":"18b257ab-f8e8-4b36-bc2d-b945c221a355","resolution":{"observed_at":"2026-08-06T11:46:00.745262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.733911Z","title":"Less-vfl: Communication-efficient feature selection for vertical federated learning,","venue":null,"work_id":"6a8db6ae-4c66-4d9e-ab0c-06f84579389a","year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.095130Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:4de11ac4d51d86033d6676bebc12e0ab5bca45d9ee171a4d5d86c42d928c1480","observation_id":"0abe2e64-7f0d-4ad6-b798-c347f1f2fa93","resolution":{"observed_at":"2026-08-06T11:46:00.737160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.724736Z","title":"Label inference attacks against vertical federated learning,","venue":null,"work_id":"175060b7-179b-4fe3-8b38-909de08dc4a2","year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.098127Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:45c6e4df77434cd85c58f041c9f3df920a12b63261985f5b2cbb3abae61a8638","observation_id":"21f16f50-06a8-4467-90c6-a9c56ab4dfe8","resolution":{"observed_at":"2026-08-06T11:46:00.727589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.715795Z","title":"Practical feature inference attack in vertical federated learning during prediction in artificial internet of things,","venue":null,"work_id":"4eab0233-51cf-4b5f-b7e4-7d81a9ec9092","year":2023},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.102573Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:59ded4954ae7bc3df71893fd76c4bb9c311834b2c72c74722474005f51a1609f","observation_id":"24186fcb-dd01-4492-8785-aadc5fa4f3df","resolution":{"observed_at":"2026-08-06T11:46:00.718738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.02246","last_updated":"2018-02-06T22:10:14Z","snapshot_observed_at":"2026-07-06T06:22:08.261855Z","submitted_at":"2018-02-06T22:10:14Z","title":"Approximation Methods for Bilevel Programming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.02246","snapshot_observed_at":"2026-08-06T11:45:59.105558Z","title":"Approximation methods for bilevel program- ming,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.105558Z"},"links":{"cited_paper":"/paper/1802.02246","citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:9dacaad3fb5896f4e3a4f320a3615a69d0697ce893ba728d181400f4f3890711","observation_id":"e16d27f0-9bc4-4e9a-92e1-f7038bd7981f","resolution":{"observed_at":"2026-08-06T11:45:59.105558Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.707083Z","title":"Convergence of meta- learning with task-specific adaptation over partial parameters,","venue":null,"work_id":"5d7bc94e-4939-4a4a-8575-a2af9c563986","year":2020},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.109948Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:6d33f3e83a14c7f574f772ee15f63b742503ee0c3c181a189144b92f674c80d6","observation_id":"ea56169b-28b7-46da-8e9a-111ef43a392b","resolution":{"observed_at":"2026-08-06T11:46:00.710210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.697246Z","title":"Closing the convergence gap of sgd without replacement,","venue":null,"work_id":"53731150-1f4b-4b97-b1a2-8612a409dc4f","year":2020},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.113139Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:5a035a1afe3e1e5e78176e83cf592a4a63eea089ce5f52721fc4b7fd10a52259","observation_id":"4368138e-24d9-4c55-aac1-9f19d201ec17","resolution":{"observed_at":"2026-08-06T11:46:00.700527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.687247Z","title":"Bilevel optimization: Convergence analysis and enhanced design,","venue":null,"work_id":"1d4ffd0e-5ee9-4911-9f9d-26e6d0d473fd","year":2021},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.116347Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:b4155efec8bb9c3f0d06e845af7bb51b5a7ce2e847e70dddedcd964e472fd96a","observation_id":"45b31715-1114-41d4-a1fb-3d02a8ad6b9e","resolution":{"observed_at":"2026-08-06T11:46:00.691274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.677126Z","title":"Fastslowmo: Federated learning with combined worker and aggregator momenta,","venue":null,"work_id":"e13012cd-0116-427e-b504-fa3d30fad6e0","year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.119717Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:efc00cbee7ab1441bd7a6ad987f3ddffe6bf6e1aa0133b1518dc69fef1ca20f5","observation_id":"d3c34287-9655-4ff1-bed3-aa87bbe1b75b","resolution":{"observed_at":"2026-08-06T11:46:00.681377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.666276Z","title":"General data protection regulation,","venue":null,"work_id":"debc037b-0be6-4c3f-90d4-e325fa3b98bb","year":2020},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.124306Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:56c32d348ab11498550a414447124d9fbf2d922f46216b1550f28c640ecb955c","observation_id":"c46b0ed9-77a0-4165-8951-8719be90e846","resolution":{"observed_at":"2026-08-06T11:46:00.670160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.656980Z","title":"Inverting gradients-how easy is it to break privacy in federated learning?","venue":null,"work_id":"225b89e6-3e4e-4b60-87ff-84227b8e363c","year":2020},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.128335Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:7c2a92b1f5e2c3d486f719ec141b0d673b188329a50d64450062e97b11b46c21","observation_id":"d42d6f06-4732-4492-96dc-d6284f8f3486","resolution":{"observed_at":"2026-08-06T11:46:00.660286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.646295Z","title":"3d shapenets: A deep representation for volumetric shapes,","venue":null,"work_id":"d4f9327a-dba8-4bdc-ab93-246f6387a894","year":2015},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.131786Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:717a20999be57b8eed90516c8ac6870870b1be3b5823e624e7577307a57391ae","observation_id":"29fe5cda-d171-4cb6-a0ad-849f3809af1b","resolution":{"observed_at":"2026-08-06T11:46:00.649497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.637372Z","title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,","venue":null,"work_id":"5166a5e8-88de-443f-9366-2c1c53f01219","year":2017},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.135075Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:6ce599ae90879cbc771286202630fe3e182b61f09968264b79dc79cd433d6ff1","observation_id":"9bb2134f-58a5-4e83-a376-a6c2e9574202","resolution":{"observed_at":"2026-08-06T11:46:00.640292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:59.138329Z","title":"Default of Credit Card Clients,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.138329Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:b31adda0729b8aaf739ef5b827f2545b9742c424e183083b40c89552a8d5a3ed","observation_id":"699b0aee-af7f-4cd0-9495-54dae02250df","resolution":{"observed_at":"2026-08-06T11:45:59.138329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:45:59.141691Z","title":"Becker and R","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.141691Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:b57a3c64596d724050baa0fce6d928ab8569e255a592b9a86362ef5740f0b355","observation_id":"8394728a-7454-41c4-8fee-6755f68f958b","resolution":{"observed_at":"2026-08-06T11:45:59.141691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.627829Z","title":"A method for stochastic optimization,","venue":null,"work_id":"63e9a74a-6fad-48a0-99ec-47edb78b3439","year":2015},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.145393Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:54ecf5a5306204003b9c6ea4dd1551dc68d244312ea6a40a8bd133eff21a2fbb","observation_id":"7c161396-f952-47ac-9aba-2105dcc9c2e7","resolution":{"observed_at":"2026-08-06T11:46:00.631350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.617516Z","title":"An experimental study of class imbalance in federated learning,","venue":null,"work_id":"c7be5e13-0cc3-4e8d-bb2c-cb9e8a87449a","year":2021},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.148700Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:8536513c5f4e7a2a6641f91bb957bb03c222e45550655281c7d94145d9ef6731","observation_id":"7e3a88c6-a518-4b11-b794-4c1adc3d9bd3","resolution":{"observed_at":"2026-08-06T11:46:00.621369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.607065Z","title":"Semantic cosine similar- ity,","venue":null,"work_id":"d013bdd2-9562-4116-8d2b-8d0e5d26d578","year":2012},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.152063Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:68aab4e5b5a300de7b34dc8da757e496b8e10ab76f76bcd192da3950434dc799","observation_id":"d94b314f-711a-476f-966b-50cca4dcdd0b","resolution":{"observed_at":"2026-08-06T11:46:00.610542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.596165Z","title":"Learning with a wasserstein loss,","venue":null,"work_id":"70e84c73-6cc2-4c2e-bf2c-4e874760b4d0","year":2015},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.155451Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:f470d9d8f8527fccd8f21b55affb72ddcccb9588d42fb8eacd8d21fff774cab1","observation_id":"610503d5-692c-4398-ab78-273ad32134cf","resolution":{"observed_at":"2026-08-06T11:46:00.599979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.585462Z","title":"Semi-supervised cross-silo advertising with partial knowledge transfer,","venue":null,"work_id":"2d7787a7-f67d-4c5d-9a10-90fd84995045","year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.158317Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:0414c3d01b03b70a06e36f67b8b67f1e1c9f070ed4c3f10b0c781cb5ec9cb8f7","observation_id":"c9adcccb-5f1a-44b6-8ee9-2723b46912da","resolution":{"observed_at":"2026-08-06T11:46:00.589168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.576133Z","title":"Differential privacy,","venue":null,"work_id":"a58fd3e4-0e0f-4cac-bc21-e2c7b6650a94","year":2006},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.161590Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:9e647040126642f1a17b2dedadaa1fc23efd1da61609892ae60d0b302f62d726","observation_id":"934502b1-c3b4-4bd7-9aee-b45eaf202f5b","resolution":{"observed_at":"2026-08-06T11:46:00.579434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.565909Z","title":"Semi-supervised learning by entropy minimization,","venue":null,"work_id":"bae6bd06-947b-4a94-86fd-f5b851039447","year":2004},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.165380Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:4498c432b5d739b894c78f2261600479f41668a7b1a915908cd6b19c45b49272","observation_id":"f1064a29-d076-4c9a-9f69-af57d51ca604","resolution":{"observed_at":"2026-08-06T11:46:00.569428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.555515Z","title":"Semi- supervised domain adaptation via minimax entropy,","venue":null,"work_id":"ce062899-c89e-49a7-aa8a-a8d4321952ca","year":2019},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.168595Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:de886d67848a69b5e7d93a9455dc8155444e16144be7dd11c839602d55cb2add","observation_id":"ba42142a-c51f-4479-8dd7-e56532c18544","resolution":{"observed_at":"2026-08-06T11:46:00.558884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.546026Z","title":"Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,","venue":null,"work_id":"c5db7e03-36e1-4a2f-8c67-3484964f37ec","year":2019},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.171962Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:71a4b16157a0c400740d6c82567c4b6bc95b964aa50ffee6cd0776fc3ceda601","observation_id":"4c2ca93e-9288-4da4-a3b8-569e690f5a97","resolution":{"observed_at":"2026-08-06T11:46:00.549028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.535067Z","title":"Universal domain adaptation through self supervision,","venue":null,"work_id":"c23a533e-3301-4652-926d-855bb78b5c32","year":2020},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.175500Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:5a1d0fe4b84bf4821d8321d99e8fde22ce7389f0f5901bb804bc0b0aba410a70","observation_id":"3b2d5537-c8f3-4b35-ab71-786a90911e12","resolution":{"observed_at":"2026-08-06T11:46:00.538800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.524423Z","title":"Robust optimal transport with applications in generative modeling and domain adaptation,","venue":null,"work_id":"248d8811-cf58-45b2-acbd-329e58489310","year":2020},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.178716Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:64a6b5517384e88376319a16978d5db249f7f97d0c984d2c84241d4fa9e655d9","observation_id":"76fb7c29-a8c1-4451-8a82-74a5eec436a9","resolution":{"observed_at":"2026-08-06T11:46:00.528156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.514616Z","title":null,"venue":null,"work_id":"57da1d07-a207-4f74-b0ca-aaf93ca73c67","year":2005},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.181936Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:3f782893a32b90aafce242cc4d6ee7f7c60046a32c31548690dc7ed726c0115e","observation_id":"db0b67c7-ed31-4617-8d50-3336323db867","resolution":{"observed_at":"2026-08-06T11:46:00.518146Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.504249Z","title":"Cross-silo federated neural architecture search for heterogeneous and cooperative systems,","venue":null,"work_id":"5253792b-ddca-443d-b78e-ebc28eea1856","year":2022},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.184772Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:a836d03e477836f28fa90c4505fcaac76c858638a8771e37ebd441dffc496f7b","observation_id":"a39fc52f-6778-40d5-b619-8e0870c5e110","resolution":{"observed_at":"2026-08-06T11:46:00.507715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.492253Z","title":"Likelihood","venue":null,"work_id":"ec819a9a-8f92-47c1-a1d9-7f470cbff938","year":2024},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.188171Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:4bf7e1b94f18fc91d45d076001211e244cc1a51960a7cb98837320cb861b912e","observation_id":"be6ed47d-da7d-44d8-a1d6-c188ea5a20e3","resolution":{"observed_at":"2026-08-06T11:46:00.495859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.481128Z","title":"The target label of each data point, Y m,n, can not be directly obtained by observation","venue":null,"work_id":"e07f896b-e970-47a4-b01d-d390d3373a9d","year":null},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.191944Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:aff6bec84ae027663c4f007935b67f146050ad39d307a4e61483b84bef1308ff","observation_id":"3665db38-f513-4277-bf51-242c649f5a7c","resolution":{"observed_at":"2026-08-06T11:46:00.484764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.470792Z","title":"To show the smooth property of F (Θ), we first introduce the following lemma which is proposed in [57]","venue":null,"work_id":"2e3954d9-1086-44d1-acb1-028ceb493174","year":2024},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.195741Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:3047e235e1c552d3b3141e9688a5ea10964a9a9966a810609dae8fbe35af8efe","observation_id":"07d2ecd9-54e2-474a-b35d-29f19a23a14a","resolution":{"observed_at":"2026-08-06T11:46:00.473807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.460248Z","title":"Other nota- tions involved Bj or Bj such as ∇Θ∇E llocal(Θ0 t , E j−1 t ; Bj−1) have similar meanings","venue":null,"work_id":"0ec9f864-6b90-495c-9a21-6ed1a3702c8a","year":2024},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.199282Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:f47cb77e4d6d76c54e0eae9dad681a724671972b64851430151e6ac809d42e53","observation_id":"200e3c51-f475-40b8-9af4-8ea09b7d55fb","resolution":{"observed_at":"2026-08-06T11:46:00.463598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.446933Z","title":"(77) This is a more general result of Theorem 1","venue":null,"work_id":"6be0d97d-a001-4a49-825d-1d61d86ee609","year":2024},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.202678Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:14ad74153d54e9645d804b316dbb55bb55a9f7eb07125028fd680a44e891f059","observation_id":"08aa53b7-1b6f-4e91-81c3-82e27c188bd2","resolution":{"observed_at":"2026-08-06T11:46:00.452610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.434373Z","title":null,"venue":null,"work_id":"62529bb6-d03d-4d7b-830e-5a420ac8492e","year":null},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.206279Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:48ff3cdbc6c9578b9efaafe79edb8bd5ae58db0229af75ecbeca5672d090c252","observation_id":"57d2adfc-6e09-42a1-956a-64e9dd591437","resolution":{"observed_at":"2026-08-06T11:46:00.438702Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.349060Z","title":"nc represents the number of samples in class z","venue":null,"work_id":"ab23a5b8-04ad-4292-b7a4-521b180ed5fe","year":null},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:59.210161Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:5316c6c441c8eb4fec32449bfe485ba3888f1724572904413bbf73a73e9d29af","observation_id":"caa86502-f348-4c7a-8469-d7915e487488","resolution":{"observed_at":"2026-08-06T11:46:00.388302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:46:00.961809Z","title":"Available: https://proceedings.neurips.cc/paper files/ paper/2014/file/1f1baa5b8edac74eb4eaa329f14a0361-Paper.pdf","venue":null,"work_id":"57003a34-64db-407b-854e-f33b3d892a44","year":2014},"citing_paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-06T11:45:58.894203Z"},"links":{"citing_paper":"/paper/2507.22488"},"observation_digest":"sha256:584a007d23d5884783092ae46096bd02042dcb380a358cedd165533247dbc50d","observation_id":"f34ad314-74a7-42aa-8ac2-6a49df8c75bc","resolution":{"observed_at":"2026-08-06T11:46:00.964883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.22488","last_updated":"2025-07-30T08:48:33Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T11:45:57.189488Z","submitted_at":"2025-07-30T08:48:33Z","title":"Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data"},"reference_resolution":{"displayed":88,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":7,"verified_fuzzy":60},"total_outbound_references":88},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 88 of 88 outbound references and 0 inbound Pith citation observations for arXiv:2507.22488."}