{"as_of":"2026-08-07T18:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4fc64e6cd6322425d5dbdadb8a5de19afddeddd96ab3205b01cec5afe94691b3","coverage":[{"denominator":83,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":83,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T11:55:31.884037Z","state":"measured"},{"denominator":84,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":84,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T09:22:45.579077Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-30T09:24:32.266414Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"cited_work":{"arxiv_id":"2507.22330","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.22330","snapshot_observed_at":"2026-06-30T09:24:32.266414Z","title":"arXiv preprint arXiv:2507.22330 (2025) MOSAIC 19","venue":null,"work_id":"b37a7b7d-9dbc-4951-98a2-b554578e8939","year":2025},"citing_paper":{"arxiv_id":"2606.29049","last_updated":"2026-06-27T19:10:31Z","snapshot_observed_at":"2026-07-07T00:03:02.476882Z","submitted_at":"2026-06-27T19:10:31Z","title":"MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-06-30T09:22:45.579077Z"},"links":{"cited_paper":"/paper/2507.22330","citing_paper":"/paper/2606.29049"},"observation_digest":"sha256:cb837c9a09cf46be612734af8f41b3b021955492d687dd3b07203c85212677a9","observation_id":"bddbfad0-5c0c-4a79-80ef-f73c8c12feba","resolution":{"observed_at":"2026-06-30T09:24:32.268094Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2507.22330/citation-record","integrity":"/paper/2507.22330/integrity","json":"/paper/2507.22330/citation-record.json","paper":"/paper/2507.22330"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:55:31.449492Z","title":"Adaptive segmentation enhanced asynchronous federated learning for sustainable intelligent transportation systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.449492Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:90de330054a048c4de210ff2c3fc4164231810de2e6bc082335d14db664ad814","observation_id":"4b063edb-ccd2-4c81-80a0-14d4edd48c66","resolution":{"observed_at":"2026-08-06T11:55:31.449492Z","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":"2026-08-06T11:55:31.455874Z","title":"Ai- empowered trajectory anomaly detection for intelligent transportation systems: A hierarchi- cal federated learning approach","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.455874Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:b762977806eea8a8be1f0e6cd7e70ff21d99534f3d6af9a8621018db8445e9d6","observation_id":"f18b426d-dc16-4908-8d4b-65baafff07d7","resolution":{"observed_at":"2026-08-06T11:55:31.455874Z","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":"2026-08-06T11:55:31.462198Z","title":"Reliable federated learning with gan model for robust and resilient future healthcare system","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.462198Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:ddc805f85fbfcec83c7d004b55b29402fa52a3c7d1f130d687f23532dafbd972","observation_id":"bd4967a9-f532-47c9-948f-b9187e74b843","resolution":{"observed_at":"2026-08-06T11:55:31.462198Z","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":"2026-08-06T11:55:33.325376Z","title":"Harmony: Heterogeneous multi-modal federated learning through disentangled model training","venue":null,"work_id":"2ef0a0c8-e177-4556-be94-3c648749cd7b","year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.468870Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:d2b542c633fa4a25e5ab79ecc2cc1b7546ba6c9790abfbb1c0e6ff37ce736023","observation_id":"1260a155-2bce-4881-9388-79982385fde5","resolution":{"observed_at":"2026-08-06T11:55:33.330296Z","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":"2026-08-06T11:55:31.474676Z","title":"Federated learning for smart healthcare: A survey","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.474676Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:7e123cd01f42842e8815adc35c2670bc055f6fb7292ea01780469cecc0ea522b","observation_id":"08e8b249-3af8-4790-89ed-611a728b7180","resolution":{"observed_at":"2026-08-06T11:55:31.474676Z","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":"2026-08-06T11:55:33.298630Z","title":"Robust privacy-preserving recommendation systems driven by multimodal federated learning","venue":null,"work_id":"9f81507e-bca7-43cd-8b2b-76524661bd07","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.481626Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:22381a3dfdd8da4ff8f6e05eb00e2c67249dfbee13f4ed88e36d3348cb4370d2","observation_id":"91521372-1a29-4780-aa14-0e890f84ee6b","resolution":{"observed_at":"2026-08-06T11:55:33.303686Z","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":"2026-08-06T11:55:33.282751Z","title":"Federated unlearning for on-device recommendation","venue":null,"work_id":"4bad4972-22c2-4829-9a64-1b782c0fde8d","year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.487815Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:28ed07d6e20fea4194e5d7416d7328ca7a704eb9f6512f0564d10795ceb44140","observation_id":"d67c3a78-7102-4e96-8002-29f36ec6edfd","resolution":{"observed_at":"2026-08-06T11:55:33.287981Z","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":"2026-08-06T11:55:33.266341Z","title":"Prefer: Point-of-interest recommendation with efficiency and privacy-preservation via federated edge learning","venue":null,"work_id":"e1810131-99d9-4009-b185-c33f8120a75d","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.493533Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:2145a253c5c69b08ee470b79bca1704a3aa4c1abd2dc0540e2863be75ad2fb11","observation_id":"0f82ac50-fe61-4438-bea2-7bb76da5d119","resolution":{"observed_at":"2026-08-06T11:55:33.271844Z","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":"2026-08-06T11:55:33.249235Z","title":"Federated multi-task learning","venue":null,"work_id":"89dbc8e7-76cf-4873-8062-77a7bdef20f4","year":2017},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.498341Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:72b7d969ee0c602753c040ab0277191250aea550dc0ca4f226d47325334e2f6f","observation_id":"f919e214-ad8a-4fa0-81f0-c7e3832c5cd2","resolution":{"observed_at":"2026-08-06T11:55:33.254229Z","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":"2026-08-06T11:55:33.232407Z","title":"Personalized federated learning with moreau envelopes","venue":null,"work_id":"5f999572-c402-403b-acf6-a3f3153753ae","year":2020},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.503307Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:82ae80200f3d2139a93b0ed2b66b2fed4d515590bc5e191cce467477b0c7b8d6","observation_id":"9bb3f9df-e23d-4eba-be50-dd3d1b1978ba","resolution":{"observed_at":"2026-08-06T11:55:33.237359Z","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":"2003.13461","last_updated":"2020-11-06T04:07:31Z","snapshot_observed_at":"2026-08-02T16:42:32.296692Z","submitted_at":"2020-03-30T13:19:37Z","title":"Adaptive Personalized Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.13461","snapshot_observed_at":"2026-08-06T11:55:31.508322Z","title":"Adaptive personalized federated learning","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.508322Z"},"links":{"cited_paper":"/paper/2003.13461","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:e1e7d9106110c235b682fb302b5ef5bb210b84852e52b00560ce63d1a2dd568f","observation_id":"4a161366-e5f2-4199-844b-faee9a7e5bbd","resolution":{"observed_at":"2026-08-06T11:55:31.508322Z","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":"2026-08-06T11:55:33.216666Z","title":null,"venue":null,"work_id":"866f864f-712c-41d1-83b2-eb941b91da48","year":2016},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.513921Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:f05dc0b46464369acc845e064ee84558879700dab2b8387b536a3f08c74fdbbf","observation_id":"4184215c-3c7c-4cc6-9c05-cf78bca77e58","resolution":{"observed_at":"2026-08-06T11:55:33.221658Z","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":"2026-08-06T11:55:33.201625Z","title":"Tifl: A tier-based federated learning system","venue":null,"work_id":"8a412fa5-37b5-421e-b683-950c210a64e7","year":2020},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.518942Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:597bbbe9cae7df3e5f0c9a12c47b849253505f5f84963fd35d073afb818ec0b6","observation_id":"f32263fb-ca23-4195-9df4-65c749e90b38","resolution":{"observed_at":"2026-08-06T11:55:33.206331Z","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":"2026-08-06T11:55:33.185041Z","title":"Effective heterogeneous federated learning via efficient hypernetwork-based weight generation","venue":null,"work_id":"c7d349d4-bace-46f5-a137-ec79f8da86ad","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.524412Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:b8afa546ec7202ab11cd0694f0acca844eab4d13dc7b00a8300f25edb482a1a6","observation_id":"6a34ea21-ed43-481d-9b54-1635f6179fa6","resolution":{"observed_at":"2026-08-06T11:55:33.189969Z","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":"1812.07210","last_updated":"2019-01-08T16:01:46Z","snapshot_observed_at":"2026-08-01T16:51:56.119064Z","submitted_at":"2018-12-18T07:31:18Z","title":"Expanding the Reach of Federated Learning by Reducing Client Resource Requirements","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.07210","snapshot_observed_at":"2026-08-06T11:55:31.529813Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.529813Z"},"links":{"cited_paper":"/paper/1812.07210","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:2e3a06e5e541b49c9bfac56ca45424725225fb5ecc47b89679b808eaa285232f","observation_id":"e499e0b6-db8d-4568-8b6d-0aa54bd900ad","resolution":{"observed_at":"2026-08-06T11:55:31.529813Z","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":"2026-08-06T11:55:33.168591Z","title":"Group knowledge transfer: federated learning of large cnns at the edge","venue":null,"work_id":"db56947e-2ad7-4f17-97ca-553e03e1c1b3","year":2020},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.535173Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:b9af9e538f4b2b2153d4f2dfed957a1b5b5dffe26220ddaea6fa0a83be76ca64","observation_id":"365bf923-a055-4293-aa90-514c62d521f3","resolution":{"observed_at":"2026-08-06T11:55:33.173928Z","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":"2026-08-06T11:55:33.152445Z","title":null,"venue":null,"work_id":"217b1533-d60e-45d8-ab39-55e05ba92553","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.540696Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:08a2b487ad260fdaedeecb58acb845f0b462e313bfc2a5fede13445404c79546","observation_id":"8973c9e8-b456-421a-b43b-0d4c878625dd","resolution":{"observed_at":"2026-08-06T11:55:33.157403Z","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":{"arxiv_id":"1910.03581","last_updated":"2019-10-08T18:00:00Z","snapshot_observed_at":"2026-07-06T08:27:52.322036Z","submitted_at":"2019-10-08T18:00:00Z","title":"FedMD: Heterogenous Federated Learning via Model Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.03581","snapshot_observed_at":"2026-08-06T11:55:31.545915Z","title":"Fedmd: Heterogenous federated learning via model distillation","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.545915Z"},"links":{"cited_paper":"/paper/1910.03581","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:ff15f58bbca2fa1862d784825983b33d58b777235172d89e3002987d8dbc322d","observation_id":"c5291661-0de6-42a8-b847-6bee5605ef11","resolution":{"observed_at":"2026-08-06T11:55:31.545915Z","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":"2026-08-06T11:55:33.136436Z","title":"Data-free knowledge distillation for heteroge- neous federated learning","venue":null,"work_id":"44383b5c-73ca-433a-8a1f-b0cd7419cdac","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.552063Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:5af32f10d031b4bbbd48f0721c1ba6a39ad4da44d139f188cce540f7317b1a6a","observation_id":"3c99636d-8b69-4e04-8034-483214cfe66b","resolution":{"observed_at":"2026-08-06T11:55:33.141634Z","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":"2026-08-06T11:55:33.120378Z","title":"Exploring the distributed knowledge congruence in proxy-data-free federated distillation","venue":null,"work_id":"82770b47-2143-415a-8357-3b11629ef1d9","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.557391Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:4cdfb31dc557bb796cfcc251c1b9d555e084699ffeb0be97817e109521e2c294","observation_id":"35671914-c607-43b9-acf5-18db608733e8","resolution":{"observed_at":"2026-08-06T11:55:33.125598Z","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":"2305.02776","last_updated":"2023-06-09T07:33:29Z","snapshot_observed_at":"2026-08-02T13:08:07.375150Z","submitted_at":"2023-05-04T12:21:34Z","title":"Efficient Personalized Federated Learning via Sparse Model-Adaptation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02776","snapshot_observed_at":"2026-08-06T11:55:31.562992Z","title":"Efficient personalized federated learning via sparse model-adaptation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.562992Z"},"links":{"cited_paper":"/paper/2305.02776","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:d59341b018870cd2b082e21fa474fc19a9fa3e05be98756320a45ccf9657be9d","observation_id":"94b6d5b1-5724-4555-98f2-03e95d2ad324","resolution":{"observed_at":"2026-08-06T11:55:31.562992Z","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":"2026-08-06T11:55:33.103942Z","title":"Hetero{fl}: Computation and communication efficient federated learning for heterogeneous clients","venue":null,"work_id":"601ce2bc-a015-475e-a20d-b4d9da40d0d1","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.568517Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:0bd74b7cacd94f48955dd2278dab84f0c2264616e672961943c7fa4f089e3208","observation_id":"6f34ab4d-08ec-477f-8750-e88a23b6f1fc","resolution":{"observed_at":"2026-08-06T11:55:33.109171Z","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":"2026-08-06T11:55:33.087818Z","title":"Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction","venue":null,"work_id":"dd8739b2-1416-4d4e-978a-8aaeed4c8849","year":2022},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.573756Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:a371e1c83a3680665fc155709e756527f3cf7f4bc4c8c04c37d61d8f04526ede","observation_id":"df76cfd8-a813-4685-ad06-2592347c87de","resolution":{"observed_at":"2026-08-06T11:55:33.092898Z","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":"2026-08-06T11:55:33.071756Z","title":"Efficient split-mix federated learning for on-demand and in-situ customization, 2022","venue":null,"work_id":"2ca3b8fe-542f-4e2b-b5d3-e6a0eee7a97f","year":2022},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.579534Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:6902f703235d6b383bd5c00e5192d7411666df2065a559885ed25e7c644cf391","observation_id":"b6d7aaa6-5650-4bb0-b0b7-e07491493382","resolution":{"observed_at":"2026-08-06T11:55:33.076840Z","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":"2026-08-06T11:55:33.054340Z","title":"Fedconv: A learning-on-model paradigm for heterogeneous federated clients","venue":null,"work_id":"27730997-13f7-4e64-8f80-90630466c7c8","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.584579Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:8f95111bc7eb1dc54d4c50505fdc28a12c11ea151b8997fad149eeed52c32801","observation_id":"1eca4262-e0e0-4c29-acdc-e7e1ef87d294","resolution":{"observed_at":"2026-08-06T11:55:33.059754Z","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":"2026-08-06T11:55:33.035003Z","title":"Zhang, Song Guo, Jingcai Guo, Deze Zeng, Jingren Zhou, and Albert Y","venue":null,"work_id":"d5ba67b9-aa15-46ea-9391-081638848470","year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.589426Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:b571ce378b0a343d709c2f1a0afeecc1f83871268b434ba6c49be864936c6ee3","observation_id":"d1a07f14-df8e-46ca-8a0d-6747a91f4e3c","resolution":{"observed_at":"2026-08-06T11:55:33.040931Z","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":"2026-08-06T11:55:33.016470Z","title":"Dfrd: Data-free robustness distillation for heterogeneous federated learning","venue":null,"work_id":"753fa6d9-ff1f-4f77-a460-36815535cd25","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.594272Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:b1f8e20ee7f6f260656282c2c49f287507717c0ebcef70ae22d85755d3ada93f","observation_id":"bc682e3a-cf60-40d8-829b-7b845a60550c","resolution":{"observed_at":"2026-08-06T11:55:33.022185Z","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":"2026-08-06T11:55:32.998009Z","title":"Adapter-guided knowledge transfer for heterogeneous federated learning","venue":null,"work_id":"9a1115fe-ff5d-48d7-99f8-3f57ab874057","year":2025},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.599667Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:02bd844c1d9436e6c33b27862af1013623cabf1a5f560c5c53e347dfae8f5663","observation_id":"fc368276-aeb6-4211-9c3b-c3097e86208a","resolution":{"observed_at":"2026-08-06T11:55:33.004103Z","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":"2026-08-06T11:55:32.980655Z","title":"Heteroge- neous federated learning framework for iiot based on selective knowledge distillation","venue":null,"work_id":"83d40635-cf36-4197-b2cc-89576feaf7df","year":2025},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.604457Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:bc40e208908b137e5af449a316ad6bcc4e69b854b758417c8761ac028e111e73","observation_id":"f358d4b3-fd88-458d-872e-46b91e79c048","resolution":{"observed_at":"2026-08-06T11:55:32.985947Z","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":"2026-08-06T11:55:31.609225Z","title":"Personalized federated learning with feature alignment and classifier collaboration","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.609225Z"},"links":{"cited_paper":"/paper/2306.11867","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:8116aae874c14f2d5ca34f17574b1136d824edc96030b6b378fd845e8c9d3424","observation_id":"756a3459-c8be-44aa-ab12-12a0320a8a39","resolution":{"observed_at":"2026-08-06T11:55:31.609225Z","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":"2026-08-06T11:55:32.961035Z","title":"Exploiting shared representations for personalized federated learning","venue":null,"work_id":"4c7c2878-d945-4728-bbf7-ceca7d77a77a","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.614326Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:de0e57ddf0555604700a4420ddb979148a8c3f6ff6967a940f2c4018e87badb7","observation_id":"a0802a3a-5595-4bc0-b39e-0efbafdf33f2","resolution":{"observed_at":"2026-08-06T11:55:32.967289Z","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":"1912.00818","last_updated":"2019-12-02T14:29:00Z","snapshot_observed_at":"2026-07-06T08:41:25.156353Z","submitted_at":"2019-12-02T14:29:00Z","title":"Federated Learning with Personalization Layers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.00818","snapshot_observed_at":"2026-08-06T11:55:31.619433Z","title":"Federated learning with personalization layers","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.619433Z"},"links":{"cited_paper":"/paper/1912.00818","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:21734f95244ed21a17b597b2d2dfc2211b512227fd78cb7bb6d20ab7df31fae3","observation_id":"2f8e9fa9-10f6-4c6c-ac14-42db944fc737","resolution":{"observed_at":"2026-08-06T11:55:31.619433Z","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":"2026-08-06T11:55:32.941252Z","title":"Allen, Randy P","venue":null,"work_id":"1ef21bbb-b285-4344-8b70-9d923db3dff4","year":2020},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.626456Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:9c0345528802aab6808fb4e433d0bc7d7cd844314143094d5d08b876bbbb6ee5","observation_id":"245e328d-f7d2-4cb1-8f44-15b473b171e3","resolution":{"observed_at":"2026-08-06T11:55:32.946816Z","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":"2026-08-06T11:55:32.924194Z","title":"Fedclassavg: Local representation learning for personalized federated learning on heterogeneous neural networks","venue":null,"work_id":"6713de7a-5140-4a7d-afac-4b1d1e28397e","year":null},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.631883Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:7d8bd7d6274d4afc061f9808c1c8364cce8575d1124a8dc2b849b9492249c858","observation_id":"28bd6b93-305e-4d93-87f1-cbc0ba2ab602","resolution":{"observed_at":"2026-08-06T11:55:32.929598Z","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":"2026-08-06T11:55:32.893566Z","title":"Fedgh: Heterogeneous federated learning with generalized global header","venue":null,"work_id":"55d0acd5-405e-4806-be8f-4fb6150ae11d","year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.643086Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:2845e1ab31c66d98b2230661180247c9543e2a3e8e8f0a43f8abab6e7cb436f3","observation_id":"5b1d7067-d915-46b6-9778-d19252818c22","resolution":{"observed_at":"2026-08-06T11:55:32.899066Z","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":"2311.06879","last_updated":"2023-11-12T15:43:39Z","snapshot_observed_at":"2026-08-05T10:14:27.474931Z","submitted_at":"2023-11-12T15:43:39Z","title":"pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing","version":1},"cited_work":{"arxiv_id":"2311.06879","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.06879","snapshot_observed_at":"2026-08-06T11:55:32.151062Z","title":"pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing","venue":"cs.LG","work_id":"7192a212-354b-46ea-9730-dc561af6a021","year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.648384Z"},"links":{"cited_paper":"/paper/2311.06879","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:ca1edbaff3781616fc7caf2a81786006f36cbcd6d0d8656a54922ffa82fa73c2","observation_id":"7765f729-bfa7-47d4-b596-a8a343a96528","resolution":{"observed_at":"2026-08-06T11:55:32.158012Z","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":"2026-08-06T11:55:32.875940Z","title":"Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout","venue":null,"work_id":"49010c75-fb57-47bc-afb7-bfe8c733d62d","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.653508Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:fc2872baf18dca573a90ca22466361707f495b6ca6d611d1f2bd0d78766a1e76","observation_id":"5a729362-dc13-427b-ad0c-e77f35ace4cf","resolution":{"observed_at":"2026-08-06T11:55:32.881156Z","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":"2026-08-06T11:55:32.857402Z","title":"Scalefl: Resource-adaptive federated learning with heterogeneous clients","venue":null,"work_id":"04689558-58aa-4ad8-89d8-6c355c73b195","year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.658278Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:8e81d847cec1e9e47906438072b076964829b8b2d5c18a87f1ff502dad127d22","observation_id":"825a1a95-8daf-4e29-a61d-dc757b9650fb","resolution":{"observed_at":"2026-08-06T11:55:32.863417Z","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":"2026-08-06T11:55:32.840787Z","title":null,"venue":null,"work_id":"88103ba5-d3bf-4f9f-af32-e7ed2e733294","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.663548Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:9bdac8269b83bd7bafb3b1da77afd3c63167decba9fda6d562f982b17da94cd2","observation_id":"a4483328-0b4b-40c7-88ed-160fb157133b","resolution":{"observed_at":"2026-08-06T11:55:32.845843Z","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":"2026-08-06T11:55:32.824091Z","title":"Ensemble attention distillation for privacy-preserving federated learning","venue":null,"work_id":"342a223b-d18d-41d9-9953-e1b996b9de37","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.668450Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:b4152ce88a961db43cf2a983096b5ae54fd1385340899d53840f130158a2b452","observation_id":"5169c7a3-4bfa-485b-8685-abf38e400a25","resolution":{"observed_at":"2026-08-06T11:55:32.829452Z","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":"2026-08-06T11:55:31.673357Z","title":"Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.673357Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:56f1e6610ea65bef6a7ad9dd99735fcdfc81c97d90f67aced06cd7a76ebc4db5","observation_id":"982d771d-17bb-4a3a-ad65-ba70ae082893","resolution":{"observed_at":"2026-08-06T11:55:31.673357Z","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":"2026-08-06T11:55:32.796929Z","title":"Fed-dfa: Federated distillation for heterogeneous model fusion through the adversarial lens","venue":null,"work_id":"7e093f79-2a89-4c31-a955-9ba2841d436e","year":2025},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.678388Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:3bd3c54fe2cc74b8fcf1788f3853299632b96f50a6587b79179626480f143293","observation_id":"fcf2064b-35f3-427e-bbfd-7c0dc4025901","resolution":{"observed_at":"2026-08-06T11:55:32.802013Z","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":"2026-08-06T11:55:32.780763Z","title":"Dai, and Quoc V","venue":null,"work_id":"62f7f090-b81d-4aa4-b2f0-88590bd65b3c","year":2017},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.683421Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:c2f126d22e860892a16a348c5836b9f9dbc7d36ea6f0b97a08640deea08c44b1","observation_id":"fdbe0389-178b-460f-b119-0e712c497ddc","resolution":{"observed_at":"2026-08-06T11:55:32.786428Z","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":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-06T11:55:31.688240Z","title":"Distilling the knowledge in a neural network.arXiv preprint arXiv:1503.02531, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.688240Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:2c6f8fea6dcf6234fdfad40c9162ac0bb1c8b84e651ca75d82cded02bf49a957","observation_id":"8e1fcb71-f3c3-464f-8828-abdb3e19fd25","resolution":{"observed_at":"2026-08-06T11:55:31.688240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.11479","last_updated":"2023-10-19T14:11:11Z","snapshot_observed_at":"2026-07-06T07:17:33.790455Z","submitted_at":"2018-11-28T10:16:18Z","title":"Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.11479","snapshot_observed_at":"2026-08-06T11:55:31.693430Z","title":"Communication-efficient on-device machine learning: Federated distillation and augmen- tation under non-iid private data","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.693430Z"},"links":{"cited_paper":"/paper/1811.11479","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:f235f8c8b689624de900fcd2c091fa947089822e2911e54fe189fc7fafc3e9f9","observation_id":"0a163c12-c140-45b2-8060-d1d077f74a34","resolution":{"observed_at":"2026-08-06T11:55:31.693430Z","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":"2026-08-06T11:55:32.763949Z","title":"Continual learning with hypernetworks","venue":null,"work_id":"9b3b61fb-36ea-4521-979b-f1192155a3e6","year":2020},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.698225Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:b90fa67652ea96f2f5030d24a0f96e5b5d2043623fc6008f8c6c13e617d135f0","observation_id":"4572a048-d03b-4938-8547-5b7705725fc4","resolution":{"observed_at":"2026-08-06T11:55:32.769665Z","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":"2026-08-06T11:55:32.745994Z","title":"Language modeling with recurrent highway hypernetworks","venue":null,"work_id":"70eac6c3-b533-49b4-a12c-60e4cbcb4076","year":2017},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.702985Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:142b287b2a9e461b5ae764b368ae100bfefc6efc2eb028cc69b4859eede65d94","observation_id":"6991c5b1-b81f-47b8-937e-475a2a5f086a","resolution":{"observed_at":"2026-08-06T11:55:32.750979Z","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":"2026-08-06T11:55:32.729653Z","title":"Hyperseg: Patch-wise hypernetwork for real-time semantic segmentation","venue":null,"work_id":"8da16846-9330-493b-a39f-4e123ebc543d","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.707538Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:02d3052217240201e39df2b3b6073b0a7646a0e1fa0d43881824d8fa84eebfce","observation_id":"d4b783da-3b60-4a96-8dbf-25afcd02e6ac","resolution":{"observed_at":"2026-08-06T11:55:32.734782Z","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":"2026-08-06T11:55:32.714282Z","title":"Recurrent hypernetworks are surprisingly strong in meta-rl","venue":null,"work_id":"e4073dca-9bcd-435b-b518-61bf352e71c5","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.712020Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:ef5f6a4915d01e5a56ee8234513c8771b0a072c2dd93f19c1b12cac7e7660ebf","observation_id":"c0ed220b-c009-4400-8c6f-c3a38066c9a8","resolution":{"observed_at":"2026-08-06T11:55:32.719180Z","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":"2026-08-06T11:55:32.698396Z","title":"Hyperdreambooth: Hypernetworks for fast personaliza- tion of text-to-image models","venue":null,"work_id":"44bbcc03-51d3-4d10-a1c5-d50f7c39001d","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.716770Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:c63de8e9839b05f76c7a28925f3c078a6f92821ccfd9ccf2360e15ee9df6f4fe","observation_id":"fb312587-ce7d-4633-80dc-66b532a3bef0","resolution":{"observed_at":"2026-08-06T11:55:32.703301Z","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":"2026-08-06T11:55:32.681701Z","title":"Personalized federated learning using hypernetworks","venue":null,"work_id":"5048054e-ae8d-41a2-ba04-36f43107ab61","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.721660Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:3fffa155407dc5801c69b9afe1f5a4ad2bdd395c75c892d5023f95b8ac553ca8","observation_id":"81720f7a-64cb-40e4-81ab-f68335b9961c","resolution":{"observed_at":"2026-08-06T11:55:32.687034Z","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":"2026-08-06T11:55:32.664619Z","title":"Layer-wise personalized federated learning with hypernetwork","venue":null,"work_id":"5aee4861-1111-47f1-b15b-d4da96ba5c61","year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.726196Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:66f619f98220951083636748a7ced8d619048273c9775720feb0e6238e60b881","observation_id":"824a92f3-37e2-4549-b312-a7e8fc290486","resolution":{"observed_at":"2026-08-06T11:55:32.670019Z","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":"2026-08-06T11:55:32.647450Z","title":"Pefll: Personalized federated learning by learning to learn","venue":null,"work_id":"031b402e-b773-429f-a8c0-b5d999668892","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.732643Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:6d2a9b82e62bb8a417f527948dca0f6635c896815ba569e2700728497773c45b","observation_id":"bff95eff-85ee-4a40-b7ed-29d5e5f94ab7","resolution":{"observed_at":"2026-08-06T11:55:32.652597Z","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":"2026-08-06T11:55:32.630132Z","title":"Layer-wised model aggregation for personalized federated learning","venue":null,"work_id":"abe85426-71ed-4747-8f0e-42b2c5eb40d5","year":2022},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.737972Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:f1f9a9f839d61a1b80dbc49e6b3fe36dd5246027fcbfb62d7502dfc98dd28a31","observation_id":"ec848df5-b2a0-451d-b786-c6e4f9b2d20c","resolution":{"observed_at":"2026-08-06T11:55:32.635192Z","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":"2026-08-06T11:55:32.614006Z","title":"Hypershot: Few-shot learning by kernel hypernetworks","venue":null,"work_id":"84076fd7-2b9f-4192-8ce1-08666ac1b7a0","year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.742825Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:c42f7447160e5c56159bfb3c04e0089932337e290a951d6640c6f4135678a345","observation_id":"72777ea0-eba7-4e29-9c94-bd267da73d3e","resolution":{"observed_at":"2026-08-06T11:55:32.619031Z","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":"2201.08459","last_updated":"2022-01-20T21:36:25Z","snapshot_observed_at":"2026-07-06T12:29:27.274674Z","submitted_at":"2022-01-20T21:36:25Z","title":"Federated Learning with Heterogeneous Architectures using Graph HyperNetworks","version":1},"cited_work":{"arxiv_id":"2201.08459","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.08459","snapshot_observed_at":"2026-08-06T11:55:32.074775Z","title":"Federated Learning with Heterogeneous Architectures using Graph HyperNetworks","venue":"cs.LG","work_id":"18ca01ee-a69a-4208-a751-be6676d77dc2","year":2022},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.748202Z"},"links":{"cited_paper":"/paper/2201.08459","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:a99411aba45e49f9b56bc92a72b4fc468d9861aa43dd902232c931f826448493","observation_id":"31307e4e-d82f-45c2-87ab-5a1357e57c01","resolution":{"observed_at":"2026-08-06T11:55:32.080239Z","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":"2026-08-06T11:55:32.596921Z","title":"FedBN: Federated learning on non-IID features via local batch normalization","venue":null,"work_id":"542093b2-a3de-4e85-9b9b-55df336d3ea5","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.753942Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:60d6471fcb026134a03e89809e3d227641b34304114d7f8551916ee32a59566c","observation_id":"11790c2d-d542-4722-a02f-053e96479203","resolution":{"observed_at":"2026-08-06T11:55:32.602421Z","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":"2026-08-06T11:55:32.577315Z","title":"How does a deep learning model architecture impact its privacy? a comprehensive study of privacy attacks on cnns and transformers","venue":null,"work_id":"80979cd6-7291-45a4-bce6-642beb24ca2f","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.758636Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:c52b1590f5f79a0003e8946b1ad12e80c860a8f69c25889e887592168499adf1","observation_id":"c4b8c6f9-361a-4d9d-aa72-22083eb31f7c","resolution":{"observed_at":"2026-08-06T11:55:32.583331Z","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":"2026-08-06T11:55:32.557924Z","title":"Depthfl: Depthwise federated learning for heterogeneous clients","venue":null,"work_id":"68400da6-effa-4561-8ba7-3d492c798d8e","year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.764057Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:d36ba7747af93aec7dbaa0870eedd53681d2bdddd5ab782bada31b2c6215221d","observation_id":"754dcc7d-32d6-4166-a7f8-a94180bcd1fb","resolution":{"observed_at":"2026-08-06T11:55:32.563557Z","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":"2012.00632","last_updated":"2020-12-01T16:57:25Z","snapshot_observed_at":"2026-08-01T15:12:28.545345Z","submitted_at":"2020-12-01T16:57:25Z","title":"Communication-Efficient Federated Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.00632","snapshot_observed_at":"2026-08-06T11:55:31.769468Z","title":"Communication-efficient federated distillation","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.769468Z"},"links":{"cited_paper":"/paper/2012.00632","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:a4a2be13fc84be7bc30d1cdcd49ddafb9ddcd38096979a5f9b91f411cb5ace70","observation_id":"c372941a-aed9-4c89-9cd2-1c82edddf122","resolution":{"observed_at":"2026-08-06T11:55:31.769468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.13323","last_updated":"2022-04-24T12:05:18Z","snapshot_observed_at":"2026-08-04T05:38:09.441230Z","submitted_at":"2021-08-30T15:39:54Z","title":"FedKD: Communication Efficient Federated Learning via Knowledge Distillation","version":2},"cited_work":{"arxiv_id":"2108.13323","doi":null,"metadata_source":"pith","pith_arxiv_id":"2108.13323","snapshot_observed_at":"2026-08-06T11:55:32.020811Z","title":"FedKD: Communication Efficient Federated Learning via Knowledge Distillation","venue":"cs.LG","work_id":"46593f0a-bbe5-4eaf-a165-97618e283f61","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.774766Z"},"links":{"cited_paper":"/paper/2108.13323","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:93f73a50acb33622ba578fcd9056ff598280f900598028b7bf26ef9aed547924","observation_id":"009d1175-231c-41ec-bf97-47d9e2428bd0","resolution":{"observed_at":"2026-08-06T11:55:32.029232Z","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":"2026-08-06T11:55:32.540774Z","title":"Ensemble distillation for robust model fusion in federated learning","venue":null,"work_id":"ae926e2e-138c-4829-87ab-1349df895715","year":2020},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.779820Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:3786760776234b179371d68cba634abed7760c27f9b9c12fd35065e6f24f8bf2","observation_id":"e7f912b2-6112-4206-9ca1-ce0719b56481","resolution":{"observed_at":"2026-08-06T11:55:32.546023Z","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":"2026-08-06T11:55:32.523844Z","title":"Parameterized knowledge transfer for personalized federated learning","venue":null,"work_id":"a794b4fb-7391-4615-901e-fb82825de33d","year":2021},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.784849Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:19d4f262fd2620b5d5320c99266a14bae20dd6a5cf0ef8eb640366cd82983281","observation_id":"b05ff824-c75b-41ba-9443-0b92ec82719a","resolution":{"observed_at":"2026-08-06T11:55:32.529153Z","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":"2026-08-06T11:55:32.507012Z","title":"Dense: Data-free one-shot federated learning","venue":null,"work_id":"56a42d89-0017-4af2-87cf-527fa156006e","year":2022},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.789881Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:7e1d93364d6bece186b6f591409d28ee5130aec586b2fbb7ddebd06a0853cd34","observation_id":"cb8613c7-6388-4f6a-938d-4a374c7bd5c3","resolution":{"observed_at":"2026-08-06T11:55:32.512255Z","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":"2402.15070","last_updated":"2024-02-23T03:15:10Z","snapshot_observed_at":"2026-08-04T23:21:21.392830Z","submitted_at":"2024-02-23T03:15:10Z","title":"Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.15070","snapshot_observed_at":"2026-08-06T11:55:31.794855Z","title":"Enhanc- ing one-shot federated learning through data and ensemble co-boosting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.794855Z"},"links":{"cited_paper":"/paper/2402.15070","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:3ef14a9bb7d8dde0ca604ebf0a43d16c9fb0dc60664eea14c2bb8cc17de0afb6","observation_id":"6d9831b5-87e9-4de8-91fb-9c22a31905ec","resolution":{"observed_at":"2026-08-06T11:55:31.794855Z","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":"2026-08-06T11:55:32.488218Z","title":"Fedzkt: Zero-shot knowledge transfer towards resource-constrained federated learning with heterogeneous on-device models","venue":null,"work_id":"0f7c0731-631d-4475-b18c-bbd729b8802d","year":2022},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.799962Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:cb0718f14c09fcac51ae29479e660b84d5bd326595f04a22f77f6cb84cf93496","observation_id":"c62f8b8b-6b9d-49ea-aa6b-a31ee4119c45","resolution":{"observed_at":"2026-08-06T11:55:32.493786Z","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":"2026-08-06T11:55:31.805033Z","title":"Generative adversarial networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.805033Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:4f69f8666455b241802bc08e02662aa7533f265cd4c7fb15134e97cf82248e3e","observation_id":"717cb9b2-ef91-4621-ade9-843728452fe0","resolution":{"observed_at":"2026-08-06T11:55:31.805033Z","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":"2026-08-06T11:55:32.459560Z","title":"Dfrd: data- free robustness distillation for heterogeneous federated learning","venue":null,"work_id":"37cbac01-60d3-4fff-954f-b22f353626c2","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.809768Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:cbd0fcf50ad6585f1d7d770b086b69ee8920c8cec09030f1b217f9c8313dff84","observation_id":"fa0b5627-666e-4e7a-b234-9f6650f1b452","resolution":{"observed_at":"2026-08-06T11:55:32.465282Z","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":"2026-08-06T11:55:32.442400Z","title":"$i$-divergence geometry of probability distributions and minimization problems","venue":null,"work_id":"c0194f24-86e4-4a54-aabf-2d730cc4ed8b","year":1975},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.814577Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:03d73480ba2a36da2bcd7cfc57f3aebd60def8fc13872e745c8452b964642676","observation_id":"73e31086-ad30-4ffa-a01a-d047016a3a18","resolution":{"observed_at":"2026-08-06T11:55:32.447784Z","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":"2026-08-06T11:55:32.425015Z","title":"EMNIST: extending MNIST to handwritten letters","venue":null,"work_id":"685b0290-6fdb-46a6-a697-b9fbe1221a7e","year":2017},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.819421Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:2ef7755692e2a7bfbaac9cbd61babc64657eb6ca04f44c7c47bbdc604d74a99f","observation_id":"5f6338b5-f37c-4e4e-9f40-58bda1f24dfb","resolution":{"observed_at":"2026-08-06T11:55:32.430716Z","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":"2026-08-06T11:55:32.408479Z","title":"Krizhevsky and G","venue":null,"work_id":"b88e3643-3ab0-4165-b530-179c11d632ea","year":2009},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.824254Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:444e302425370cae8558ebc6af6d5d0e6c56734a674328ed933b077c87979ba5","observation_id":"6e764d9e-5bc7-436f-878a-9a150b52fff4","resolution":{"observed_at":"2026-08-06T11:55:32.413711Z","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":"2026-08-06T11:55:31.829040Z","title":"Tiny imagenet visual recognition challenge","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.829040Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:6d7e1fe96a2a807d2b700bfdeb70f0c565af22ac47779a90ea52a7f1107dda93","observation_id":"da0cd35a-b889-465e-9606-2da0cf8385b6","resolution":{"observed_at":"2026-08-06T11:55:31.829040Z","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":"2026-08-06T11:55:31.834280Z","title":"Federated learning on non-iid data silos: An experimental study","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.834280Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:77e30dad65e68fcd5fe8cdf708c833dc0b9113b39edc90bd986ba58affe52723","observation_id":"6296054a-87c3-4c67-8121-64a24e041b23","resolution":{"observed_at":"2026-08-06T11:55:31.834280Z","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":"2026-08-06T11:55:32.366137Z","title":"Fedlpa: One-shot federated learning with layer-wise posterior aggregation","venue":null,"work_id":"8e2490c2-207c-475e-a78f-d6452a3af588","year":2024},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.838987Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:ed5ec2cd803969c290efc59709cbd7dc37c2a738fbe9002da89b10ceb57a3319","observation_id":"26bceac6-59a3-48d7-a039-b891112349c2","resolution":{"observed_at":"2026-08-06T11:55:32.372532Z","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":"2026-08-06T11:55:31.845076Z","title":"Gradient-based learning applied to document recognition","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.845076Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:e766fd156c2429485e90a8824a16f9598a7b42b2ef72455b227a8812c29ab8dd","observation_id":"c78eec29-91b5-4c18-a412-cf174f7dd8cd","resolution":{"observed_at":"2026-08-06T11:55:31.845076Z","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":"2026-08-06T11:55:32.328520Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":"28b48425-885c-40bb-bb58-5c2b3df5912c","year":2015},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.850206Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:3c691a4ba6e953a5b6730b4bf636f12c4b5d24112811f9204b417779b83f8e78","observation_id":"0d8adad1-9bb9-4d89-9116-bddb69ed8c80","resolution":{"observed_at":"2026-08-06T11:55:32.336782Z","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":"2026-08-06T11:55:32.311983Z","title":"Deep residual learning for im- age recognition","venue":null,"work_id":"2ec6dc21-b937-47e4-9801-25a26ae05b2e","year":2016},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.856490Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:b6de39e65a4edad5c68fd8b19a4a2a5d83f7b78d0408fc736ffaf33e80f5b6e0","observation_id":"bf82da11-d789-4b80-9373-21306ab40ebf","resolution":{"observed_at":"2026-08-06T11:55:32.317156Z","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":"1605.07146","last_updated":"2017-06-14T06:06:48Z","snapshot_observed_at":"2026-08-01T16:47:50.594148Z","submitted_at":"2016-05-23T19:27:13Z","title":"Wide Residual Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.07146","snapshot_observed_at":"2026-08-06T11:55:31.861400Z","title":"Wide residual networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.861400Z"},"links":{"cited_paper":"/paper/1605.07146","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:7fdff5b3200df822cdfe08016857f8d5bc7dffb4b186deb040398294a1dc12d6","observation_id":"6c0e9a66-41de-427d-8538-7325e02c87ec","resolution":{"observed_at":"2026-08-06T11:55:31.861400Z","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":"2026-08-06T11:55:31.866899Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.866899Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:50c56f66b1b75b52bfc079a00e3890987da866ac8387909e04986b80b1cca997","observation_id":"c610f713-64db-4dad-bd15-0d9f2e526030","resolution":{"observed_at":"2026-08-06T11:55:31.866899Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-06T11:55:31.872063Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.872063Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:17c704c0405b029158a6c6f406a807d1bbb6289573864faad8ada6a42c55b8b0","observation_id":"26ce51fe-6a55-46a2-856d-ea4ab42afa56","resolution":{"observed_at":"2026-08-06T11:55:31.872063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1602.07360","last_updated":"2016-11-04T21:26:08Z","snapshot_observed_at":"2026-07-06T04:47:10.710916Z","submitted_at":"2016-02-24T00:09:45Z","title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.07360","snapshot_observed_at":"2026-08-06T11:55:31.878096Z","title":"Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.878096Z"},"links":{"cited_paper":"/paper/1602.07360","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:9cbdd8c5ac84e090747ba7850da43c764566fb79fffb7bc30c7bcc3d4ff1bc65","observation_id":"e4956ecc-63d0-4485-a339-9d7f066771e9","resolution":{"observed_at":"2026-08-06T11:55:31.878096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.02610","last_updated":"2020-01-08T16:45:09Z","snapshot_observed_at":"2026-08-06T23:49:37.129499Z","submitted_at":"2020-01-08T16:45:09Z","title":"iDLG: Improved Deep Leakage from Gradients","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.02610","snapshot_observed_at":"2026-08-06T11:55:31.884037Z","title":"idlg: Improved deep leakage from gradients","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.884037Z"},"links":{"cited_paper":"/paper/2001.02610","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:6e2351e584464532cbd95a7402cda7207b197cafae09e8f0dde096a2a253f24b","observation_id":"8b6b1013-0c0b-4f4f-8733-ef2009a49fbb","resolution":{"observed_at":"2026-08-06T11:55:31.884037Z","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":"2026-08-06T11:55:31.637225Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.637225Z"},"links":{"citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:bf52989260030edb332dbe8b514a2d637b5fc57a7bd6768725895bcdfbfd20d4","observation_id":"c5df9a32-d8a0-4e58-b396-9dd87d243f66","resolution":{"observed_at":"2026-08-06T11:55:31.637225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T17:39:32.281655Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning"},"reference_resolution":{"displayed":83,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":28,"verified_exact":3,"verified_fuzzy":52},"total_outbound_references":83},"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 83 of 83 outbound references and 1 inbound Pith citation observation for arXiv:2507.22330."}