{"as_of":"2026-08-11T19:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4497e77672d342eba05ac1aa62cb2578689a158af173b9726a586f04355823cd","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:44:11.391693Z","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":"2026-07-04T04:39:35.019824Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2110.13057","last_updated":"2022-03-18T05:52:49Z","snapshot_observed_at":"2026-07-06T12:01:28.878483Z","submitted_at":"2021-10-25T15:52:06Z","title":"Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13057","snapshot_observed_at":"2026-08-10T22:44:11.391693Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.00842","last_updated":"2025-03-26T15:35:20Z","snapshot_observed_at":"2026-08-11T13:55:23.298995Z","submitted_at":"2025-01-01T13:46:11Z","title":"A Survey of Secure Semantic Communications","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T22:44:11.391693Z"},"links":{"cited_paper":"/paper/2110.13057","citing_paper":"/paper/2501.00842"},"observation_digest":"sha256:655eb43496c07fdc1eef58f01e568bfec6753fdff096a4961e8ef27f016ba08e","observation_id":"b34d7c93-3f9d-4df6-9fb0-02807f1d3c3e","resolution":{"observed_at":"2026-08-10T22:44:11.391693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13057","last_updated":"2022-03-18T05:52:49Z","snapshot_observed_at":"2026-07-06T12:01:28.878483Z","submitted_at":"2021-10-25T15:52:06Z","title":"Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13057","snapshot_observed_at":"2026-08-10T14:05:45.506545Z","title":"Robbing the fed: Directly obtaining private data in federated learning with modified models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.15718","last_updated":"2025-01-27T01:06:23Z","snapshot_observed_at":"2026-08-10T16:48:24.868687Z","submitted_at":"2025-01-27T01:06:23Z","title":"CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T14:05:45.506545Z"},"links":{"cited_paper":"/paper/2110.13057","citing_paper":"/paper/2501.15718"},"observation_digest":"sha256:a2ee9c690ee2bcdabb6b5894a9f6cc18b7f5419097e0babb2e51a2492c482656","observation_id":"a77a8abc-865a-45fb-a430-7db2f5062bb2","resolution":{"observed_at":"2026-08-10T14:05:45.506545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13057","last_updated":"2022-03-18T05:52:49Z","snapshot_observed_at":"2026-07-06T12:01:28.878483Z","submitted_at":"2021-10-25T15:52:06Z","title":"Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13057","snapshot_observed_at":"2026-08-08T20:30:46.357741Z","title":"Robbing the fed: Directly obtaining pri- vate data in federated learning with modified models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05041","last_updated":"2025-02-07T16:08:20Z","snapshot_observed_at":"2026-08-11T13:10:14.280693Z","submitted_at":"2025-02-07T16:08:20Z","title":"Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T20:30:46.357741Z"},"links":{"cited_paper":"/paper/2110.13057","citing_paper":"/paper/2502.05041"},"observation_digest":"sha256:5fc8e43d761ca2638d700464455a39482b899060681ecdce38edac545cb2c426","observation_id":"cd5b3fd7-6f09-4a33-b7a5-de293c771d5f","resolution":{"observed_at":"2026-08-08T20:30:46.357741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13057","last_updated":"2022-03-18T05:52:49Z","snapshot_observed_at":"2026-07-06T12:01:28.878483Z","submitted_at":"2021-10-25T15:52:06Z","title":"Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models","version":2},"cited_work":{"arxiv_id":"2110.13057","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.13057","snapshot_observed_at":"2026-07-04T04:39:35.019824Z","title":"Robbing the fed: Directly obtaining private data in federated learning with modi- fied models.arXiv preprint arXiv:2110.13057, 2021","venue":null,"work_id":"24fca3f5-0a52-485a-b57e-07e178e06ae1","year":2021},"citing_paper":{"arxiv_id":"2606.20553","last_updated":"2026-06-18T17:58:25Z","snapshot_observed_at":"2026-08-10T06:20:19.076347Z","submitted_at":"2026-06-18T17:58:25Z","title":"From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T16:51:07.028013Z"},"links":{"cited_paper":"/paper/2110.13057","citing_paper":"/paper/2606.20553"},"observation_digest":"sha256:ebb5365346f5de7ce7725742e92e70ead34a728a7c3c0d0752ac8fdb8eca03b8","observation_id":"569fbc1a-4eb9-4374-9a70-e2bbcb433954","resolution":{"observed_at":"2026-07-04T04:39:35.021336Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13057","last_updated":"2022-03-18T05:52:49Z","snapshot_observed_at":"2026-07-06T12:01:28.878483Z","submitted_at":"2021-10-25T15:52:06Z","title":"Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13057","snapshot_observed_at":"2026-08-02T02:54:03.989583Z","title":"Robbing the Fed: directly obtaining private data in federated learning with modified models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.14205","last_updated":"2026-07-15T17:57:53Z","snapshot_observed_at":"2026-08-09T04:05:40.464409Z","submitted_at":"2026-07-15T17:57:53Z","title":"Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-02T02:54:03.989583Z"},"links":{"cited_paper":"/paper/2110.13057","citing_paper":"/paper/2607.14205"},"observation_digest":"sha256:c10bd1b6f4716a962ede7c44b385f4286184449379e3ea97f824969f92b054e8","observation_id":"50427c0e-df0f-4ea4-90a3-fcef341eda1e","resolution":{"observed_at":"2026-08-02T02:54:03.989583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2110.13057/citation-record","integrity":"/paper/2110.13057/integrity","json":"/paper/2110.13057/citation-record.json","paper":"/paper/2110.13057"},"outbound":[],"paper":{"arxiv_id":"2110.13057","last_updated":"2022-03-18T05:52:49Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T12:01:28.878483Z","submitted_at":"2021-10-25T15:52:06Z","title":"Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2110.13057."}