{"as_of":"2026-08-09T18:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e7bb308f56d20f0b9465bf20c2dc9422579bc1859a9290761a7fe3ef923cef9b","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T12:47:02.067693Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2502.07456/citation-record","integrity":"/paper/2502.07456/integrity","json":"/paper/2502.07456/citation-record.json","paper":"/paper/2502.07456"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1609.09106","last_updated":"2016-12-01T10:08:15Z","snapshot_observed_at":"2026-08-08T11:39:44.107967Z","submitted_at":"2016-09-27T05:57:00Z","title":"HyperNetworks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.09106","snapshot_observed_at":"2026-08-08T12:47:02.010825Z","title":"Dai, and Quoc V","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.010825Z"},"links":{"cited_paper":"/paper/1609.09106","citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:d714b2fc70cc87d499447324629e46f6eb54a92394aed7d10a9a0c4b312470bc","observation_id":"3e3620a0-1ec1-4ddf-9ef6-33e8c2d5f6b4","resolution":{"observed_at":"2026-08-08T12:47:02.010825Z","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":"2026-08-08T12:47:02.705248Z","title":"Personalized cross-silo federated learning on non-iid data","venue":null,"work_id":"49c3d59c-ac46-4af2-b1c5-4d2fc0807d28","year":2021},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.015328Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:9f8350491715bf3f774a5828b70749b4571b3e82742b2fd8a2854b88d75ad651","observation_id":"856f9cc0-0e33-4127-b475-88b64ff41030","resolution":{"observed_at":"2026-08-08T12:47:02.708197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.696544Z","title":"Model-contrastive federated learning","venue":null,"work_id":"63bcd506-65c4-4467-a6cd-3cd820427b81","year":2021},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.019122Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:7fa2c88fb7a620c8a3c482d741fc64284b61d02e8381cf186afc7f4bbb082c9d","observation_id":"284633d5-369c-448d-9a93-f8f96566d7b7","resolution":{"observed_at":"2026-08-08T12:47:02.699714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.688125Z","title":"Fedphp: Federated personalization with inherited private models","venue":null,"work_id":"5fa7dedc-7b66-4c7d-84d2-9267b52ea8db","year":2021},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.023336Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:e4f95efb5a67db70ff132a6b7f04a5dbc8c81496ecee1e9a00f68b6a657d58d5","observation_id":"9452baa9-e374-4fa1-a746-c9df3b0a04a8","resolution":{"observed_at":"2026-08-08T12:47:02.691483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.678851Z","title":"Adapt to adaptation: Learning personalization for cross-silo federated learning","venue":null,"work_id":"4840bba6-e8bc-43a1-99c8-093051bb304a","year":2022},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.027247Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:da97797b07dc078e557da542c016167f36da2edbbc5b8d4406057d76a799bd09","observation_id":"3d4dee01-c851-44f0-aa37-823c6d40ca5c","resolution":{"observed_at":"2026-08-08T12:47:02.682453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.668700Z","title":"Layer-wised model aggregation for personalized federated learning","venue":null,"work_id":"70bcba53-42eb-48c7-8511-5d2b7ac2e59a","year":2022},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.030803Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:cdc8f42f610396573839ffc23f0db9fb6217e5792c4700f6f771880fee8588db","observation_id":"c4f65678-1d65-4408-b0ac-8f1306a02099","resolution":{"observed_at":"2026-08-08T12:47:02.672143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.659762Z","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","venue":null,"work_id":"eacf44b0-3ac5-471d-9528-79fd312a5056","year":2017},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.034662Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:2bd57e55a9bb14ce0d0484009695767120a21a82978d219de51aa4895506d477","observation_id":"a6dd4eb5-4220-4120-b11c-a8f7e284d074","resolution":{"observed_at":"2026-08-08T12:47:02.663119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.650346Z","title":"Personalized federated learning using hypernetworks","venue":null,"work_id":"0e2f4cef-2bc3-46bf-84a2-f086c8a29482","year":2021},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.037880Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:e1b68a27a7186dcf048387d4571a2172951ac153fa14372bcbd0eba02f7a31c3","observation_id":"eb1d7950-6eb0-49bf-af79-12011f0c9e3a","resolution":{"observed_at":"2026-08-08T12:47:02.653450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.639219Z","title":"Fedproto: Federated prototype learning across heterogeneous clients","venue":null,"work_id":"f7a6fc8e-fb5a-4344-b5a1-44ea55e80c90","year":2022},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.040827Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:584cef0769c15d96015ac3cceea1fcab9fbd28e5ca25cb8c0d51c7f8c13b4a17","observation_id":"7b141f4f-a307-4283-a703-bb4a0b1646ef","resolution":{"observed_at":"2026-08-08T12:47:02.643200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.628563Z","title":"Personalized federated learning for intelligent iot applications: A cloud-edge based framework","venue":null,"work_id":"d6e5148d-b5c5-4158-8e50-b5d867b3ac3b","year":2020},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.043837Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:a15f57605ff646e461e92f27099fdb93d6f5acd8c6a85966c1b7175441c8c44c","observation_id":"51af0c26-679c-4677-b3ca-ddcfe86b6fdd","resolution":{"observed_at":"2026-08-08T12:47:02.632310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.047218Z","title":"Personalized federated learning with feature alignment and classifier collaboration","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.047218Z"},"links":{"cited_paper":"/paper/2306.11867","citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:a84a0a10bf0604d43d9cd4f6f4026edf0412ee693e9c11f7c7dd92349a510cd4","observation_id":"b29c5960-edfd-47b9-8775-2a1df53b3495","resolution":{"observed_at":"2026-08-08T12:47:02.047218Z","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":"2026-08-08T12:47:02.618133Z","title":"Federated machine learning: Concept and applications","venue":null,"work_id":"5ae7a1f2-4ca8-45ea-99fa-cdcab72601cc","year":2019},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.051063Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:73d99568e5b9d0709da658cf4f7b1ecf555d0a315271abffd6c88473774c3926","observation_id":"32b0cae7-3dfc-472b-b9a8-62c42027b37b","resolution":{"observed_at":"2026-08-08T12:47:02.622018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.539971Z","title":"Fedgh: Heterogeneous federated learning with generalized global header","venue":null,"work_id":"b6f6934e-233b-40f9-9035-d7a925c0e97d","year":2023},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.054586Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:98cf680d28d5bdbafb38f87197853653b0db42436a8f7b1d64d99b71efbf7213","observation_id":"45fe5d1e-a879-4d1d-9289-dde58c408690","resolution":{"observed_at":"2026-08-08T12:47:02.573281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.458406Z","title":"B ayesian nonparametric federated learning of neural networks","venue":null,"work_id":"f64e06f1-976f-4a3b-8c48-bf8a61a45e50","year":2019},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.058036Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:488cf50c09e043387bb5985fd3fc204ca3c9dce738963deb04cbf52194f47af3","observation_id":"38e226e1-c070-4d4b-b34e-3c1d6ddd3765","resolution":{"observed_at":"2026-08-08T12:47:02.495942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.381244Z","title":"Fedala: Adaptive local aggregation for personalized federated learning","venue":null,"work_id":"f127b5f5-6a0c-4759-8255-c4ccb088c78c","year":2023},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.061002Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:0b1f7e29f6d9b979cf6f93bed2a4b122e85b4e9d511a19d29c61213f2004c1b8","observation_id":"3cacc515-3d95-441a-85f7-7cf593d40f4b","resolution":{"observed_at":"2026-08-08T12:47:02.412413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:47:02.244015Z","title":"Eliminating domain bias for federated learning in representation space","venue":null,"work_id":"0d08fe1b-7805-4792-8168-7c14d5f1fef0","year":2024},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.064463Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:8d5607e204fc11ead4d1b6b660a724e96a12a0aaebfd25e1e8922066fd00ab7e","observation_id":"22ac47db-045f-4de4-8c72-abd1205a0dee","resolution":{"observed_at":"2026-08-08T12:47:02.288070Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:47:02.067693Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T12:47:02.067693Z"},"links":{"citing_paper":"/paper/2502.07456"},"observation_digest":"sha256:1dc0b61417d61d8548aa5d3c734d0a6878fe87d08e30736dc422a703b68e6dfb","observation_id":"c110446a-b7de-4cfb-9529-91eb1c9ca733","resolution":{"observed_at":"2026-08-08T12:47:02.067693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.07456","last_updated":"2025-02-15T15:40:47Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T12:38:55.810560Z","submitted_at":"2025-02-11T11:00:58Z","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":13},"total_outbound_references":17},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2502.07456."}