{"as_of":"2026-08-22T03:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:56ee13ee6c1ee1e2824ec4e3bd86754940d56faecf5c2376bb684ab75f8378a5","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:04:05.475979Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.11097/citation-record","integrity":"/paper/2505.11097/integrity","json":"/paper/2505.11097/citation-record.json","paper":"/paper/2505.11097"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:04:06.229896Z","title":"The right to be forgotten","venue":null,"work_id":"2e5cf978-8d1e-42b9-9c6f-d3aa912446ca","year":2011},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.274767Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:ec921723f86da9754a59cf30872b9b5068e482d0b11e8bcefdc400917938193d","observation_id":"e04f01b2-df9b-450a-b7b0-213d07c77fa0","resolution":{"observed_at":"2026-08-15T21:04:06.234643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:05.283510Z","title":"The california consumer privacy act: Towards a european-style privacy regime in the united states","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.283510Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:32659785a873f8d4e74b374dd9aa239cefd73ed46061efa3fd6bd052184f66e5","observation_id":"0b83492a-1b69-4c92-837b-c87302a43ada","resolution":{"observed_at":"2026-08-15T21:04:05.283510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.13891","last_updated":"2021-05-06T04:50:42Z","snapshot_observed_at":"2026-08-16T18:55:51.920608Z","submitted_at":"2020-12-27T08:54:37Z","title":"Federated Unlearning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.13891","snapshot_observed_at":"2026-08-15T21:04:05.292160Z","title":"Federated unlearning","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.292160Z"},"links":{"cited_paper":"/paper/2012.13891","citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:3d60d9638d8f2cc23f2c0a26065826700cc1cd17bb723d9378934ed916cb2f29","observation_id":"c54c3e39-ab32-4542-b1d7-8cf695b588b2","resolution":{"observed_at":"2026-08-15T21:04:05.292160Z","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":"2409.1816","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:04:05.799287Z","title":"Federated unlearning in financial applications","venue":null,"work_id":"e4dc6988-5f7a-4e7b-9926-112ace23126f","year":2024},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.302542Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:0c5bd098c046c2eccf65ede5ec395a1a7f02307c1d1cb1ded737953dc2b698c6","observation_id":"ce610347-3cc0-43da-8de0-25924a3ce147","resolution":{"observed_at":"2026-08-15T21:04:05.807046Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:05.311918Z","title":"Verifi: Towards verifiable federated unlearning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.311918Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:d24c80986c766967f7b2fe5cf560f0cb7854f1532697768bc6fedc0f92184b17","observation_id":"31e2fca0-c604-4ae2-a73a-debcf453b0a8","resolution":{"observed_at":"2026-08-15T21:04:05.311918Z","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":"2026-08-15T21:04:06.194560Z","title":"Proof of unlearning: Definitions and instantiation","venue":null,"work_id":"c2807dbe-fcd1-49bc-be0d-39cbba3f251a","year":2024},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.321794Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:8ba45de2dfdcb31962a2cdbcdff9549bdcb9d7891e2836854c7cfa1d8361c4b5","observation_id":"e5d2af98-2584-41c5-bc2c-0e9cdf037b30","resolution":{"observed_at":"2026-08-15T21:04:06.199835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:06.178557Z","title":"Feder- ated learning with blockchain-enhanced machine unlearning: A trustworthy approach","venue":null,"work_id":"a5fcb716-fcba-406c-862c-de8a1a580f6b","year":2025},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.327187Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:fa8b08afc471a56ba7665dc3939c13c1d21bb88b198035dc18e73c8e13176ff7","observation_id":"ab5f6de2-5f5e-411a-9bb2-828191107a4f","resolution":{"observed_at":"2026-08-15T21:04:06.183634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:06.163036Z","title":"In29th USENIX security symposium (USENIX Security 20), pages 1291–1308, 2020","venue":null,"work_id":"7d3570bc-a752-4e9a-bb9f-155487bd63e6","year":2020},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.332006Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:db75a97faa404f59b531714481448a7278784778dabc36cbf611e9fcdee07a0d","observation_id":"89cf37b6-4f8b-4e6c-a84f-6c02ef1bc55f","resolution":{"observed_at":"2026-08-15T21:04:06.168210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.09441","last_updated":"2022-01-24T03:56:20Z","snapshot_observed_at":"2026-08-20T00:18:38.637691Z","submitted_at":"2022-01-24T03:56:20Z","title":"Federated Unlearning with Knowledge Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.09441","snapshot_observed_at":"2026-08-15T21:04:05.336362Z","title":"Federated unlearning with knowledge distillation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.336362Z"},"links":{"cited_paper":"/paper/2201.09441","citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:8b7f236f6dacab5ba9c215770268ac332549de1da154cf7c7cf1a0b63a8bbb63","observation_id":"abf5d009-fba6-49da-a9ca-cb76d6cc6922","resolution":{"observed_at":"2026-08-15T21:04:05.336362Z","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":"2026-08-15T21:04:06.149502Z","title":"Federated unlearning via class-discriminative pruning","venue":null,"work_id":"e5c8c96b-ebc1-458d-9d43-380b8bc21da8","year":2022},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.341072Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:cf71ae8f2b845ac434c4d2ece57d58aadd459d144750d9c0eef44703bf53edee","observation_id":"3f8b5ed5-2adc-4e68-8bbb-96569cc9c5e5","resolution":{"observed_at":"2026-08-15T21:04:06.153543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.20709","last_updated":"2025-02-28T04:35:26Z","snapshot_observed_at":"2026-08-16T13:42:40.643310Z","submitted_at":"2025-02-28T04:35:26Z","title":"Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter","version":1},"cited_work":{"arxiv_id":"2502.20709","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.20709","snapshot_observed_at":"2026-08-15T21:04:05.704230Z","title":"Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter","venue":"cs.LG","work_id":"2b57038e-bd1d-4e0d-8860-e0a0c9705ccb","year":2025},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.345489Z"},"links":{"cited_paper":"/paper/2502.20709","citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:aa27119e12e1cc3b8d6068d945f65a40bdb170a072f06d2da32e1fb8988c8b15","observation_id":"0efb5df2-bc48-48ec-89fa-4554554716b5","resolution":{"observed_at":"2026-08-15T21:04:05.709767Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:06.135312Z","title":"When the curious abandon honesty: Federated learning is not private","venue":null,"work_id":"ef22ff76-4c41-4b1e-a4df-3d41e083d514","year":2023},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.350426Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:cda95c42f09a0f986ae6d0f3bd7c4316cf60d07190f8b23972d19a1d444210bb","observation_id":"a92d6e86-af43-47f4-afed-87b82a346dca","resolution":{"observed_at":"2026-08-15T21:04:06.139814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:06.119954Z","title":"Privacy-preserving federated learning with malicious clients and honest-but-curious servers","venue":null,"work_id":"859265cb-4dd8-41fc-8c09-0c85731b7065","year":2023},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.355231Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:3caf6e69103402f0a7ac9222ab63be841e2f582ad1c3a1214622ababecfa3585","observation_id":"a1e34037-afd6-4f9b-8f8e-da5da6ddbb3d","resolution":{"observed_at":"2026-08-15T21:04:06.124952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:05.359629Z","title":"Deep leakage from gradients","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.359629Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:766fcbc4e3e075b24605f0d353e54b162fdb544428bb42841dd27f4386a98b89","observation_id":"43c68b53-a428-47fb-a9e1-5f5c9941511d","resolution":{"observed_at":"2026-08-15T21:04:05.359629Z","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":"2026-08-15T21:04:06.094933Z","title":"Inverting gradients- how easy is it to break privacy in federated learning?Advances in neural information processing systems, 33:16937–16947, 2020","venue":null,"work_id":"e6fd86ed-79a8-40c7-a496-e81f6496e078","year":2020},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.363824Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:74f99bdc8539e88a61c77c6b97c3bb28e039dd0b8d49c07a5e192dc8ea742898","observation_id":"00464b6f-03cf-407b-b565-844058cf7c4b","resolution":{"observed_at":"2026-08-15T21:04:06.100034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:06.079715Z","title":"Learn what you want to unlearn: Unlearning inversion attacks against machine unlearning","venue":null,"work_id":"63ddd245-9a12-43a7-b8f1-1c76299788fb","year":2024},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.368103Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:1cad13ca36320160af154d7c251c0f879a2a7f067d35e502e078af05f4c829ae","observation_id":"b8634583-0326-40dc-b593-c96a33ce06cb","resolution":{"observed_at":"2026-08-15T21:04:06.084626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:06.064527Z","title":"On the necessity of auditable algorithmic definitions for machine unlearning","venue":null,"work_id":"2e0a2fcc-a19b-4b1d-88d6-cc5c85672a79","year":2022},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.372335Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:50ac7de48c2ef8cdf52b26b688c72493560cf761ba2985bec287f51ac83eb35c","observation_id":"cb13e445-8116-4adb-858a-16535b37e6c8","resolution":{"observed_at":"2026-08-15T21:04:06.069488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:05.376453Z","title":"Auditing privacy defenses in federated learning via generative gradient leakage","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.376453Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:0232ecc9b585d06bca2be00d05505d347050f022e2493daee6f517c5e1edeb76","observation_id":"eb5785b1-bf19-463d-926d-3c2868714979","resolution":{"observed_at":"2026-08-15T21:04:05.376453Z","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":"2026-08-15T21:04:06.038355Z","title":"The right to be forgotten in feder- ated learning: An efficient realization with rapid retraining","venue":null,"work_id":"7dbd56f3-ac64-44db-aa5e-2b3de97840fe","year":2022},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.380742Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:ea15fe7f6b9c5429cd430726c014e1a3f7748374273e891903ab8915707a855c","observation_id":"2e5fccfb-c82f-41e1-8591-04f37706b4c6","resolution":{"observed_at":"2026-08-15T21:04:06.042904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05521","last_updated":"2023-10-20T19:57:54Z","snapshot_observed_at":"2026-08-21T13:26:26.289952Z","submitted_at":"2022-07-12T13:24:23Z","title":"Federated Unlearning: How to Efficiently Erase a Client in FL?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05521","snapshot_observed_at":"2026-08-15T21:04:05.385077Z","title":"Federated unlearning: How to efficiently erase a client in fl?, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.385077Z"},"links":{"cited_paper":"/paper/2207.05521","citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:3c6ca930a08f8a6ba6d56aeffd2f8aa55fd1ad22696be10e86d66794bef560ae","observation_id":"3351a970-dd75-4732-920a-d8d1e3266a66","resolution":{"observed_at":"2026-08-15T21:04:05.385077Z","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":"2026-08-15T21:04:06.022965Z","title":"Fedu: Federated unlearning via user-side influence approximation forgetting","venue":null,"work_id":"76e45b0e-e49f-4e32-8166-9ef9dbbe3a3f","year":2024},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.389966Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:58674ddee2c3228a37e85749d757fca4ca7dc342e928d474d016146f82b45495","observation_id":"3ddb78a0-e4f8-49c3-8df0-fca5c9c39ef4","resolution":{"observed_at":"2026-08-15T21:04:06.027646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:06.007367Z","title":"Update selective parameters: Federated machine unlearning based on model explanation","venue":null,"work_id":"9cd993f1-897e-4a13-aa46-31ea050d8571","year":2024},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.394481Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:159310c6627305cb3572b803e77242d3eb6609cf8d5cf1ad9d098eaaa9196864","observation_id":"95ecc487-aae0-4210-856d-cd5b3a44e1c6","resolution":{"observed_at":"2026-08-15T21:04:06.012547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.15474","last_updated":"2025-03-27T12:41:08Z","snapshot_observed_at":"2026-08-17T01:49:59.415897Z","submitted_at":"2024-05-24T11:53:13Z","title":"Unlearning during Learning: An Efficient Federated Machine Unlearning Method","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.15474","snapshot_observed_at":"2026-08-15T21:04:05.398663Z","title":"Unlearning during learning: An efficient federated machine unlearning method","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.398663Z"},"links":{"cited_paper":"/paper/2405.15474","citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:51002b827bf8ebb73fe5c317704cd40c258e466d488b61aa76e1dddee6154702","observation_id":"f12c3056-99d2-45af-bd11-52c687ec088b","resolution":{"observed_at":"2026-08-15T21:04:05.398663Z","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":"2026-08-15T21:04:05.991554Z","title":"Fedmua: Exploring the vulnerabilities of federated learning to malicious unlearning attacks","venue":null,"work_id":"0dde425a-338f-45a8-878f-ff0f8b55cd66","year":2025},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.403703Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:82d19830887e4d0af3ff3554d3fddb121dcc049a447248295c50ebb476c6ec03","observation_id":"28cf0d0b-2d6b-4deb-93fe-47bcbe785752","resolution":{"observed_at":"2026-08-15T21:04:05.996615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.17396","last_updated":"2025-01-29T03:23:46Z","snapshot_observed_at":"2026-08-16T13:42:23.998492Z","submitted_at":"2025-01-29T03:23:46Z","title":"Poisoning Attacks and Defenses to Federated Unlearning","version":1},"cited_work":{"arxiv_id":"2501.17396","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.17396","snapshot_observed_at":"2026-08-15T21:04:05.648861Z","title":"Poisoning Attacks and Defenses to Federated Unlearning","venue":"cs.CR","work_id":"90039b9b-cb69-44d0-bb39-f5c70d75c021","year":2025},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.408202Z"},"links":{"cited_paper":"/paper/2501.17396","citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:05e92f038411028a48dd8eac96610f10dc4949d07a612364a89afed0baa39925","observation_id":"e232818d-be1b-4fb8-8f40-59189e6b8b24","resolution":{"observed_at":"2026-08-15T21:04:05.655963Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:05.975750Z","title":"Generative gradient inversion via over-parameterized networks in federated learning","venue":null,"work_id":"67efb826-016f-4a6d-955d-c16d644b7573","year":2023},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.413367Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:ce585054017ec2643b17a5a9040cd9dc2e7712d8648163c9627c1c65c41cc2a7","observation_id":"58cf9513-2ed8-49d1-aac1-5d5ab6ef9ef1","resolution":{"observed_at":"2026-08-15T21:04:05.980973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:05.417810Z","title":"Gradient inversion with generative image prior","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.417810Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:1675d6cb29d88822a237208afc74fc6a5c046e166e39f4c00452f3ae751bb571","observation_id":"fcf78300-c7ea-4dd0-90da-ccac47b1c5f9","resolution":{"observed_at":"2026-08-15T21:04:05.417810Z","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":"2026-08-15T21:04:05.950630Z","title":"Gifd: A generative gradi- ent inversion method with feature domain optimization","venue":null,"work_id":"0d58960e-a736-4771-b0ec-fd5a3a4c454f","year":2023},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.423095Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:2ba696bed05341ac679ab7f682d81a41b35cfcdc8d32f12f1242048ed6303a81","observation_id":"b4e2713e-5ef7-46af-baad-e8c7ac5eeafd","resolution":{"observed_at":"2026-08-15T21:04:05.955351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:05.936333Z","title":null,"venue":null,"work_id":"419465dd-b3b3-479f-955b-c46d05832e05","year":2024},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.427635Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:95b97e18db871227296b52cc58b2755e0cf80cd1dfaa595db62322a060e9c285","observation_id":"e8c09fbf-21a9-496c-a218-cd1ca7123dd4","resolution":{"observed_at":"2026-08-15T21:04:05.940446Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:05.432098Z","title":"Learning to invert: Simple adaptive attacks for gradient inversion in federated learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.432098Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:94515359c402ee07235e1de54898757e3f30589ec2855129ae07b2706dac7cfc","observation_id":"6329f447-d834-409f-951a-bdb074770307","resolution":{"observed_at":"2026-08-15T21:04:05.432098Z","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":"2026-08-15T21:04:05.436069Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.436069Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:91b2657a89aec6c3035b31bed8332247f7f5c63fea750182b29c13485cc9ecb4","observation_id":"ca9cd802-c0c7-442e-b37c-c95ff58cea5e","resolution":{"observed_at":"2026-08-15T21:04:05.436069Z","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":"2026-08-15T21:04:05.902579Z","title":"In Encyclopedia of Mathematics","venue":null,"work_id":"34c0b282-2d47-43e1-bf26-8bd8147571f4","year":2001},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.440165Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:b7957be56ae061480224bf6d39246b1827f52846a69670deec8ed3946a43afb1","observation_id":"1c32e7f9-2aa3-40c7-8ea6-b5d1bf4982d9","resolution":{"observed_at":"2026-08-15T21:04:05.906939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:05.888365Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"3e6d8a30-1854-488c-937c-3306f3cd07b2","year":2009},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.444100Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:a37801a0e46795baea6f380b5d086c4c676e780be1e8d8b9160e6935fec8b8eb","observation_id":"552b1cb6-1673-4a5d-9961-56956d6eaeff","resolution":{"observed_at":"2026-08-15T21:04:05.892836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:04:05.448329Z","title":"Gradient-based learning applied to document recognition","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.448329Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:0b9c8f29f89b205a20a92d60e5129f7223f5c47a8c3a8019d5251b2255396697","observation_id":"5eafa901-3451-473e-b888-fbe49ad4757c","resolution":{"observed_at":"2026-08-15T21:04:05.448329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-08-13T15:13:33.081929Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-15T21:04:05.452340Z","title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.452340Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:7c7d63a6cb935034db654958ad6eaffc07a25ef59db23f09e68ed949642ac88e","observation_id":"616ffc8e-7dd0-4aa3-a6b8-0617427414ff","resolution":{"observed_at":"2026-08-15T21:04:05.452340Z","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":"2026-08-15T21:04:05.457986Z","title":"The unrea- sonable effectiveness of deep features as a perceptual metric","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.457986Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:dc7dabf7d0bd212e4ff1d7314bfa693793d87fb58c08ba11e9063c79741b27ba","observation_id":"f76d63e5-a115-4c0e-8ffd-28af4f7497f3","resolution":{"observed_at":"2026-08-15T21:04:05.457986Z","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":"2026-08-15T21:04:05.462611Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.462611Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:f68c99fe307cb18686b1fcddcb03a7e9ea28db7be11063ecddd55407bb08a939","observation_id":"a47c6a30-81ce-4f50-8be4-248d5879cfea","resolution":{"observed_at":"2026-08-15T21:04:05.462611Z","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":"2026-08-15T21:04:05.467092Z","title":"Federated optimization in heterogeneous networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.467092Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:35a4b2820639b84a89c24dbded9fcab9059c55c159319ba30e44ad47961b735b","observation_id":"68130b34-7f52-4466-b84c-431e42465dcd","resolution":{"observed_at":"2026-08-15T21:04:05.467092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.00295","last_updated":"2021-09-08T23:37:17Z","snapshot_observed_at":"2026-08-13T06:06:10.844945Z","submitted_at":"2020-02-29T16:37:29Z","title":"Adaptive Federated Optimization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.00295","snapshot_observed_at":"2026-08-15T21:04:05.471298Z","title":"Adaptive federated optimization","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.471298Z"},"links":{"cited_paper":"/paper/2003.00295","citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:3b0a33c114c1664573d357cb7d375b8267f348ff13409bce6e129ee6808c7ef5","observation_id":"9b8b107b-61fc-484e-a6a9-43d57e8f14e2","resolution":{"observed_at":"2026-08-15T21:04:05.471298Z","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":"2026-08-15T21:04:05.835691Z","title":"Feature hashing for large scale multitask learning","venue":null,"work_id":"d6a5cbc3-5d6b-4213-845a-43858a15520a","year":2009},"citing_paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T21:04:05.475979Z"},"links":{"citing_paper":"/paper/2505.11097"},"observation_digest":"sha256:a36c9c66fbfeb196fbac9461a8e093baed7bb937b8653504a3dabdebcd4e4f36","observation_id":"e7334438-9f56-42b8-92fb-12f0510a74cf","resolution":{"observed_at":"2026-08-15T21:04:05.841200Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.11097","last_updated":"2025-05-16T10:28:30Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-21T15:19:25.126466Z","submitted_at":"2025-05-16T10:28:30Z","title":"Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":3,"verified_fuzzy":18},"total_outbound_references":40},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.11097."}