{"as_of":"2026-08-23T13:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7226290e27964ac58aa9a9138e0c943ed3c162312541fe1d8ec17f4eaf834dae","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":"2026-08-23T06:30:58.430688+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":"2026-08-16T11:50:19.699026Z","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":"2026-05-12T03:11:18.908292Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.08211","last_updated":"2024-07-07T03:57:12Z","snapshot_observed_at":"2026-08-16T17:41:35.175517Z","submitted_at":"2021-11-16T03:20:37Z","title":"FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08211","snapshot_observed_at":"2026-08-11T04:45:42.323389Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.18460","last_updated":"2025-05-17T02:40:42Z","snapshot_observed_at":"2026-08-15T05:12:50.907837Z","submitted_at":"2024-12-24T14:39:47Z","title":"GeFL: Model-Agnostic Federated Learning with Generative Models","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T04:45:42.323389Z"},"links":{"cited_paper":"/paper/2111.08211","citing_paper":"/paper/2412.18460"},"observation_digest":"sha256:890a98ac96b43cf061ba51bbcd6260fa0d475b2be25426e4a003f62b632da4a4","observation_id":"f50c3980-3bb4-4687-b064-d185b9f7c57a","resolution":{"observed_at":"2026-08-11T04:45:42.323389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08211","last_updated":"2024-07-07T03:57:12Z","snapshot_observed_at":"2026-08-16T17:41:35.175517Z","submitted_at":"2021-11-16T03:20:37Z","title":"FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08211","snapshot_observed_at":"2026-08-16T11:50:19.699026Z","title":"Fedcg: Leverage conditional gan for protecting privacy and maintaining competitive performance in federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.14628","last_updated":"2025-04-20T14:10:02Z","snapshot_observed_at":"2026-08-21T20:47:24.646011Z","submitted_at":"2025-04-20T14:10:02Z","title":"GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-16T11:50:19.699026Z"},"links":{"cited_paper":"/paper/2111.08211","citing_paper":"/paper/2504.14628"},"observation_digest":"sha256:91afd71b34324535620161a22892ba8e2d3c5fd8de262c38659d0e037bd79d93","observation_id":"2f506d94-04bc-4170-9a89-4e5ba76114ce","resolution":{"observed_at":"2026-08-16T11:50:19.699026Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08211","last_updated":"2024-07-07T03:57:12Z","snapshot_observed_at":"2026-08-16T17:41:35.175517Z","submitted_at":"2021-11-16T03:20:37Z","title":"FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08211","snapshot_observed_at":"2026-08-06T22:59:37.323613Z","title":"Fedcg: Leverage conditional gan for protecting privacy and maintaining competitive performance in feder- ated learning.arXiv preprint arXiv:2111.08211, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.20245","last_updated":"2025-06-25T08:42:10Z","snapshot_observed_at":"2026-08-16T13:30:10.950518Z","submitted_at":"2025-06-25T08:42:10Z","title":"FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T22:59:37.323613Z"},"links":{"cited_paper":"/paper/2111.08211","citing_paper":"/paper/2506.20245"},"observation_digest":"sha256:03e3c150c9fb0f88da8309e552b32f1088097c63ae08d5ccabacdfb6365a7a21","observation_id":"4d1c7e3f-cd8a-4d03-8c1c-b8271c224d29","resolution":{"observed_at":"2026-08-06T22:59:37.323613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08211","last_updated":"2024-07-07T03:57:12Z","snapshot_observed_at":"2026-08-16T17:41:35.175517Z","submitted_at":"2021-11-16T03:20:37Z","title":"FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08211","snapshot_observed_at":"2026-08-06T22:52:40.666203Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.20431","last_updated":"2025-06-25T13:42:30Z","snapshot_observed_at":"2026-08-13T22:48:54.094753Z","submitted_at":"2025-06-25T13:42:30Z","title":"Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T22:52:40.666203Z"},"links":{"cited_paper":"/paper/2111.08211","citing_paper":"/paper/2506.20431"},"observation_digest":"sha256:c3d6d445eb1deb36ca07a36a6abdff5d210b4d99c90b4d4b6e03a2a64a7944e6","observation_id":"b3604f1e-245b-4e73-b9ff-5ffeec53e961","resolution":{"observed_at":"2026-08-06T22:52:40.666203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08211","last_updated":"2024-07-07T03:57:12Z","snapshot_observed_at":"2026-08-16T17:41:35.175517Z","submitted_at":"2021-11-16T03:20:37Z","title":"FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning","version":3},"cited_work":{"arxiv_id":"2111.08211","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.08211","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning","venue":null,"work_id":"b83e60a4-6d3c-41b8-a1bd-0c72d90f6227","year":2022},"citing_paper":{"arxiv_id":"2605.08760","last_updated":"2026-05-09T07:45:10Z","snapshot_observed_at":"2026-08-15T19:08:05.840310Z","submitted_at":"2026-05-09T07:45:10Z","title":"FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-12T03:10:05.446367Z"},"links":{"cited_paper":"/paper/2111.08211","citing_paper":"/paper/2605.08760"},"observation_digest":"sha256:3c7167c252dda6cd97e2638257d9705ee0ddfa476e434ab64bdd8ecf2ba7deac","observation_id":"d323fca7-e2f8-468c-880c-01bf2252962e","resolution":{"observed_at":"2026-05-12T03:11:18.910398Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2111.08211/citation-record","integrity":"/paper/2111.08211/integrity","json":"/paper/2111.08211/citation-record.json","paper":"/paper/2111.08211"},"outbound":[],"paper":{"arxiv_id":"2111.08211","last_updated":"2024-07-07T03:57:12Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T17:41:35.175517Z","submitted_at":"2021-11-16T03:20:37Z","title":"FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2111.08211."}