{"as_of":"2026-08-15T17:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:19dae30b07a8c29135d798cf72257b9831c720e89c52cc0573344ee4a618d6f2","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T21:50:58.149123Z","state":"measured"},{"denominator":45,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":45,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2412.04521/citation-record","integrity":"/paper/2412.04521/integrity","json":"/paper/2412.04521/citation-record.json","paper":"/paper/2412.04521"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:50:58.808779Z","title":"Challenges and future directions of secure federated learning: A survey,","venue":null,"work_id":"f0416e88-53de-459d-a7c2-4711d27dd62b","year":2022},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.943001Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:ff445c5da61810ba80c44d924518b22b90e0b9ad260f6a4c83a910859e67a90d","observation_id":"9f623482-b946-480f-8037-79c856d267ea","resolution":{"observed_at":"2026-08-11T21:50:58.813665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.794565Z","title":"Advances and open problems in federated learning,","venue":null,"work_id":"b7231beb-49cf-49b6-8537-8569eebe5211","year":2021},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.948452Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:3794c2bea3ba1f88083ae98a6a2391c5c36142c4bdbd708de474afc34c17c92c","observation_id":"df1324db-2c2a-444e-8092-a8bfa1a00589","resolution":{"observed_at":"2026-08-11T21:50:58.799088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.766674Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":"7a42505e-0071-4432-b852-447f86ae4ebc","year":2017},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.958307Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:82c6392763e0614024d52be783191ce9cd24186318404636771b2f63a257bbe6","observation_id":"969b621c-bfdb-4079-a2fc-1e300115fee0","resolution":{"observed_at":"2026-08-11T21:50:58.771232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02329","last_updated":"2024-12-16T15:42:53Z","snapshot_observed_at":"2026-08-14T09:27:56.385865Z","submitted_at":"2024-01-04T16:06:31Z","title":"Exploring Vacant Classes in Label-Skewed Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02329","snapshot_observed_at":"2026-08-11T21:50:57.963192Z","title":"Not all minorities are equal: Empty-class-aware distillation for heterogeneous federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.963192Z"},"links":{"cited_paper":"/paper/2401.02329","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:5e71fa97817027fda768989fab7dde6f2050a2753504bc90444986fad1222efd","observation_id":"545c6e29-4150-4a81-8065-be551b25d551","resolution":{"observed_at":"2026-08-11T21:50:57.963192Z","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":"2026-08-11T21:50:58.752046Z","title":"Federated optimization in heterogeneous networks,","venue":null,"work_id":"29b14795-bf7e-4baa-beb2-ccde572db129","year":2020},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.968351Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:181b7df7fb029d762409f790d974f5069c0b2fab5b8c2ddc716b42cfe27e460a","observation_id":"92c74e55-32a7-4a20-af38-9a1ed9b65b0d","resolution":{"observed_at":"2026-08-11T21:50:58.756839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.780762Z","title":"Heterogeneous feder- ated learning: State-of-the-art and research challenges,","venue":null,"work_id":"4d27048c-8e3f-48fd-bb44-f7e76c796941","year":2023},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.973870Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:a8db54949a808378c16930cdbbdb93674a3bbf5633f15c56f9c870f6b1c16b74","observation_id":"a2eb0be9-b4f0-426b-bc64-015d9671f051","resolution":{"observed_at":"2026-08-11T21:50:58.785280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00582","last_updated":"2022-07-21T12:33:15Z","snapshot_observed_at":"2026-08-15T04:44:38.370440Z","submitted_at":"2018-06-02T04:45:58Z","title":"Federated Learning with Non-IID Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00582","snapshot_observed_at":"2026-08-11T21:50:57.978292Z","title":"Federated learning with non-IID data,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.978292Z"},"links":{"cited_paper":"/paper/1806.00582","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:ce78c3099c966895904363923c704985e8fefc50b4b7b07dc526f7ff5c110215","observation_id":"f05315b8-0a9a-4db8-93a9-d60609255271","resolution":{"observed_at":"2026-08-11T21:50:57.978292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.08648","last_updated":"2023-12-14T04:07:49Z","snapshot_observed_at":"2026-08-13T05:02:47.817745Z","submitted_at":"2023-12-14T04:07:49Z","title":"CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.08648","snapshot_observed_at":"2026-08-11T21:50:57.983235Z","title":"Clip-guided federated learning on heterogeneous and long-tailed data,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.983235Z"},"links":{"cited_paper":"/paper/2312.08648","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:49ad48620870e552f58c99088964376d412ab215f76eef196b77b39fa08b6008","observation_id":"bb87fcde-218a-429d-be0c-401fdbc7ae51","resolution":{"observed_at":"2026-08-11T21:50:57.983235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.09217","last_updated":"2020-02-19T15:51:25Z","snapshot_observed_at":"2026-08-15T12:59:21.783827Z","submitted_at":"2019-10-21T09:03:19Z","title":"Decoupling Representation and Classifier for Long-Tailed Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.09217","snapshot_observed_at":"2026-08-11T21:50:57.988479Z","title":"Decoupling representation and classifier for long-tailed recognition,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.988479Z"},"links":{"cited_paper":"/paper/1910.09217","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:459be58ec72986221fe3e38a681306d108e69a0d2becabc341ead5694296a010","observation_id":"b70d4a0f-d536-480d-a493-898945d275e3","resolution":{"observed_at":"2026-08-11T21:50:57.988479Z","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":"2026-08-11T21:50:58.736700Z","title":"FedGH: Heterogeneous federated learning with generalized global header,","venue":null,"work_id":"e4ae1223-30ff-4294-ba75-63fefe7a127c","year":2023},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.993857Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:5486ef9de619f4b665d351bb965e1b4db14416200626107c86c9d20e103cf617","observation_id":"a0b2edc0-5589-4e8d-8643-a56511fb4491","resolution":{"observed_at":"2026-08-11T21:50:58.742167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.720664Z","title":"FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning,","venue":null,"work_id":"28cfb0a4-9864-4d1d-9a7a-f97d0a2ce696","year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:57.998520Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:2851f8057e737769c9c6608678f55565c302714ab1656d05915f7fc9b6abc460","observation_id":"f49bd388-5f66-426b-b7b6-81164d837477","resolution":{"observed_at":"2026-08-11T21:50:58.725448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.705990Z","title":"Fedx: Unsupervised federated learning with cross knowledge distillation,","venue":null,"work_id":"29616dce-f7ee-4306-96f5-832d94ab2fed","year":2022},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.003073Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:9a0fdaf74329df4203d8748e568478f826068f6ab86c49b45fe77a0a003e4352","observation_id":"92679634-7e52-478a-94e0-561bddbfd74e","resolution":{"observed_at":"2026-08-11T21:50:58.710804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.691246Z","title":"Fedproto: Federated prototype learning across heterogeneous clients,","venue":null,"work_id":"c917f288-5514-4cfe-9786-17cf963a1808","year":2022},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.007667Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:06572845241e495987a2573caadc1743b4732509c6d67e35cf4f1be7a45926ee","observation_id":"9f8f9a42-ef7d-4e66-9b9f-97797fae5803","resolution":{"observed_at":"2026-08-11T21:50:58.696252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.676294Z","title":"PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees,","venue":null,"work_id":"c68e6425-2423-4bb4-8ce1-02b47b7357fc","year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.011988Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:3321e9e43a2b863cf62172fccf7a38a797c218fd2776bc49bf653cdc88d272a0","observation_id":"e2cfe264-8969-42fa-a260-7f9df5dc6f3c","resolution":{"observed_at":"2026-08-11T21:50:58.681324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06822","last_updated":"2024-05-10T21:52:27Z","snapshot_observed_at":"2026-08-14T08:57:10.255550Z","submitted_at":"2024-05-10T21:52:27Z","title":"MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06822","snapshot_observed_at":"2026-08-11T21:50:58.016241Z","title":"MH-pFLID: Model heterogeneous personalized federated learning via injection and distillation for medical data analysis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.016241Z"},"links":{"cited_paper":"/paper/2405.06822","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:e9127d740c72e64f546d65600d0f3e9a8d3ef4ce316f3ce8d1662f593c7b5b53","observation_id":"8619c319-15fd-468e-b822-3442a8d0a721","resolution":{"observed_at":"2026-08-11T21:50:58.016241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.11479","last_updated":"2023-10-19T14:11:11Z","snapshot_observed_at":"2026-08-14T17:52:08.881217Z","submitted_at":"2018-11-28T10:16:18Z","title":"Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.11479","snapshot_observed_at":"2026-08-11T21:50:58.020880Z","title":"Communication-efficient on-device machine learning: Federated dis- tillation and augmentation under non-IID private data,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.020880Z"},"links":{"cited_paper":"/paper/1811.11479","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:8cd95b24d6696aaa768239f63c28d3ef0a0b9d51907854e223727b687442b505","observation_id":"a2db0012-6ecd-4e4d-b8b8-f4e16ecb5df8","resolution":{"observed_at":"2026-08-11T21:50:58.020880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-08-14T22:57:08.956233Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-11T21:50:58.025836Z","title":"Distilling the knowledge in a neural network,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.025836Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:30009ec5645b0168badf7c887bdebe817e41c2bf950ecc8e0a06ae064f965767","observation_id":"0936c415-8af6-4add-9842-c6202c4d5919","resolution":{"observed_at":"2026-08-11T21:50:58.025836Z","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":"2026-08-11T21:50:58.661522Z","title":"Federated learning based on dynamic regularization,","venue":null,"work_id":"ec4a7db7-dc4a-480e-b659-948d2d2ec898","year":2021},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.030505Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:7c91b43290aecdc3b799b700cb2b6ee16156f4a7cae5884750cee34eec0bdc3d","observation_id":"dd5c4f7b-b77c-4913-bd27-c310b58b1eb7","resolution":{"observed_at":"2026-08-11T21:50:58.666270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.035286Z","title":"Model-contrastive federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.035286Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:b6f5777af587b4a0760387b689c3c7378d79275a2f4d79c5cb708e6572c4302e","observation_id":"3c1c1747-b06a-4047-bd53-04db59cee6c7","resolution":{"observed_at":"2026-08-11T21:50:58.035286Z","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":"2026-08-11T21:50:58.637080Z","title":"Deep leakage from gradients,","venue":null,"work_id":"c786a174-111e-404d-95ea-6018582974c6","year":2019},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.039514Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:cba880bc5c104c8e5b8b433f994a8f91f770f9609815342393c981d6fc55eb9d","observation_id":"6beed1b9-b423-4a4a-a9a3-a5b175815e07","resolution":{"observed_at":"2026-08-11T21:50:58.641731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.622304Z","title":"Momentum contrast for unsupervised visual representation learning,","venue":null,"work_id":"fa1bacfe-ed61-4117-9755-ec3c4f06fc8d","year":2020},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.044009Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:044fa1a20b879e2b4c78e8dca9cc4db3d30051b02459ddf21c1ae46869a5aca4","observation_id":"2078f1f0-021d-4ae4-80b3-b5fb1b651bbd","resolution":{"observed_at":"2026-08-11T21:50:58.627345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.607889Z","title":"Debiased contrastive learning for sequential recommendation,","venue":null,"work_id":"d500c207-d127-423a-8209-dfcb0b67f52d","year":2023},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.048493Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:ae3b8db166123422dd427ba5b56e52e054711bed30eca1f0c09606c325957d10","observation_id":"739c4e22-621a-4f72-8f42-92216225ec04","resolution":{"observed_at":"2026-08-11T21:50:58.612634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10011","last_updated":"2025-08-18T14:44:09Z","snapshot_observed_at":"2026-08-14T08:56:43.645018Z","submitted_at":"2024-01-18T14:27:01Z","title":"CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Retrieval","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10011","snapshot_observed_at":"2026-08-11T21:50:58.052887Z","title":"CPCL: Cross-modal prototypical contrastive learning for weakly supervised text-based person re-identification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.052887Z"},"links":{"cited_paper":"/paper/2401.10011","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:45438cca6006335bcf9de877f97586b0d0e09f16bfc2880043e02da7d2114181","observation_id":"bf2287aa-9f7f-41f8-b0af-a0f802108546","resolution":{"observed_at":"2026-08-11T21:50:58.052887Z","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":"2026-08-11T21:50:58.592348Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":"0e172947-af26-489c-b694-6a004519e458","year":2020},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.057455Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:a58e560e27c2fd94cdc84ba2350151b8014addfa8ae0871fac355cebe27a15a5","observation_id":"db9e01e0-c78e-4110-8081-8a8294390a4c","resolution":{"observed_at":"2026-08-11T21:50:58.597846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.577420Z","title":"An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning,","venue":null,"work_id":"ef3254ab-f147-4e1d-87d6-3c072ec65261","year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.061735Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:241022a30cfb97722cda46365d3970e8f623cf19d9a18dbbe7d90b294bfff607","observation_id":"f9464be4-6d4f-4cc4-a972-0d85cd412198","resolution":{"observed_at":"2026-08-11T21:50:58.582343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.561704Z","title":"Fair federated learning under domain skew with local consistency and domain diversity,","venue":null,"work_id":"a7980773-94bd-49c3-8121-c6c400ba0dc1","year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.065852Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:d5f604291622b5c2235221f6388b7ff7dbe83b1a9ce084bca964001f0a3d1f42","observation_id":"e6530f72-d07d-4f99-96e0-523a722a5e65","resolution":{"observed_at":"2026-08-11T21:50:58.567181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.546134Z","title":"Dafkd: Domain-aware federated knowledge distillation,","venue":null,"work_id":"2202597b-4a2d-445f-ac60-7ab102722723","year":2023},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.070424Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:02ad4ae7a670f7c289f0b68afbb3cd4649158ef7123db6649157ded35ae7ab14","observation_id":"90db3224-04e4-4a93-bce0-1c6f97d39c77","resolution":{"observed_at":"2026-08-11T21:50:58.551117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.531109Z","title":"Fedaux: Leveraging unlabeled auxiliary data in federated learning,","venue":null,"work_id":"3d632514-32c9-4d01-b6fa-23a890f1b597","year":2021},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.075441Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:8db8ebfbf80ea9e554d48cfc41e228650493863ebe5e1a48b5766cc457e4c532","observation_id":"8048729f-297a-41c4-a193-4973600c4f37","resolution":{"observed_at":"2026-08-11T21:50:58.536014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.516353Z","title":"Exploiting shared representations for personalized federated learning,","venue":null,"work_id":"d842f86b-9a3f-4bed-b759-096462e284ab","year":2021},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.079844Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:1d3da7c45279fdbb3691dc92d20fea4e4fe33c46f8e32164664ba91eaf281728","observation_id":"82fac9cb-6e8d-4180-965d-74c15037dbdf","resolution":{"observed_at":"2026-08-11T21:50:58.521198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.501568Z","title":"Fedala: Adaptive local aggregation for personalized federated learning,","venue":null,"work_id":"b796a50d-38a7-44fc-af05-54edf1f0a0f4","year":2023},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.084156Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:916c3342ea20a13e113ed28481cf75a5c8417a048ff84af5c1a7b768b248ce67","observation_id":"4810b17f-4282-4fd0-8da2-31f4c6e6f1c4","resolution":{"observed_at":"2026-08-11T21:50:58.506354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.00818","last_updated":"2019-12-02T14:29:00Z","snapshot_observed_at":"2026-08-13T23:51:40.504761Z","submitted_at":"2019-12-02T14:29:00Z","title":"Federated Learning with Personalization Layers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.00818","snapshot_observed_at":"2026-08-11T21:50:58.088377Z","title":"Federated learning with personalization layers,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.088377Z"},"links":{"cited_paper":"/paper/1912.00818","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:07e30907a595a9630c3d00e2ec58fda9ec7f11fc25b1d77786b354ca12958884","observation_id":"4269af8c-ced0-4009-9231-cd791908f507","resolution":{"observed_at":"2026-08-11T21:50:58.088377Z","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":"2026-08-11T21:50:58.485880Z","title":"Federated recommendation with additive personalization,","venue":null,"work_id":"3bb03268-fa3c-43a1-9f39-80af950e9b45","year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.092910Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:00e9cfa334cd8d8d4ea6c930f31cb13c2fe06d57850f1c0761361639606620fc","observation_id":"37c75308-a47d-413f-8bd3-cc66a821a56a","resolution":{"observed_at":"2026-08-11T21:50:58.490648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.097277Z","title":"Towards personalized federated learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.097277Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:9dcc28795d0e06510d2b93a1069bc641525b9617aa49bbf3a2860bdeaf466ab8","observation_id":"c981d507-2f32-4105-86e9-3a581791e6a1","resolution":{"observed_at":"2026-08-11T21:50:58.097277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.06196","last_updated":"2025-03-23T14:51:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-09T05:37:09Z","title":"Large Language Models: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06196","snapshot_observed_at":"2026-08-11T21:50:58.101401Z","title":"Large language models: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.101401Z"},"links":{"cited_paper":"/paper/2402.06196","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:8f3e4aa6f749f9c55c4f86c9be155e00e724bb0134352e81377a6d01ebea316a","observation_id":"2cad2376-961b-41a9-8cf6-09ca57fc19ca","resolution":{"observed_at":"2026-08-11T21:50:58.101401Z","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":"2026-08-11T21:50:58.461724Z","title":"A survey on large language models for recommendation,","venue":null,"work_id":"04f5b4fa-19c6-4318-9e3a-22681d17fb3d","year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.106078Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:b8c6a7bcd59e6b508ec25abe0b417b78a735b36be90fcef4dd5939888ed18c50","observation_id":"e1a59c22-8733-4dbd-aab1-11aace19dfc5","resolution":{"observed_at":"2026-08-11T21:50:58.466656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.446738Z","title":"A survey on evaluation of large language models,","venue":null,"work_id":"8f2bbba1-d42c-4f71-b2bf-430c06321a11","year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.110206Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:973b4f086b8a8a98e2cf87fe0d9687fdc397520622c92fe6cccd9c878a2da2c3","observation_id":"1a0fcb88-7f29-4ae7-b730-83f1ca68b84d","resolution":{"observed_at":"2026-08-11T21:50:58.451575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.431684Z","title":"Deep learning with differential privacy,","venue":null,"work_id":"73f246d4-4b26-4d60-9394-947d2fadf934","year":2016},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.114799Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:7f168fffca83e39292cb64c1d0b7d222791d1e2a5da231680bb82a54e4022b5f","observation_id":"4ec8618b-ced7-4d50-ada2-89db25631981","resolution":{"observed_at":"2026-08-11T21:50:58.436508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:50:58.416057Z","title":"FedUV: Uniformity and variance for heterogeneous federated learning,","venue":null,"work_id":"678db8f3-9c46-484f-b7cf-c2a3a36251cf","year":2024},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.119249Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:c2816a00f11c7322a5e88984de921d9773ec0625b5f8b391b9ee9611f4addfcc","observation_id":"e0eed171-2035-4a89-ad4f-b407bcf1322e","resolution":{"observed_at":"2026-08-11T21:50:58.420977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.06042","last_updated":"2022-03-16T03:50:49Z","snapshot_observed_at":"2026-08-13T21:48:27.045055Z","submitted_at":"2021-06-04T04:34:26Z","title":"FedBABU: Towards Enhanced Representation for Federated Image Classification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.06042","snapshot_observed_at":"2026-08-11T21:50:58.123395Z","title":"Fedbabu: Towards enhanced representation for federated image classification,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.123395Z"},"links":{"cited_paper":"/paper/2106.06042","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:260b2dae695f11c89a7004c9fafb60d3106375f663fe016e33c09402f20b0e11","observation_id":"8786042c-ca73-4cf8-8e47-4d18a915d59e","resolution":{"observed_at":"2026-08-11T21:50:58.123395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.07948","last_updated":"2020-10-23T03:04:01Z","snapshot_observed_at":"2026-08-15T15:55:09.897407Z","submitted_at":"2020-02-19T01:08:46Z","title":"Personalized Federated Learning: A Meta-Learning Approach","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.07948","snapshot_observed_at":"2026-08-11T21:50:58.127779Z","title":"Personalized federated learning: A meta-learning approach,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.127779Z"},"links":{"cited_paper":"/paper/2002.07948","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:45c57fcb1291dbc558a4c86c00db20073530a7a38f55386d10889b49d8b0a05e","observation_id":"d004a29f-fab0-449a-9c67-5c05525f8e00","resolution":{"observed_at":"2026-08-11T21:50:58.127779Z","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":"2026-08-11T21:50:58.132295Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.132295Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:0d0634f2a50a4cf2e96aab87d2fca88323a8fe03f242f1b788ef178440a21a4c","observation_id":"a3f607c6-d76f-472c-a70e-5fd146f6bfb4","resolution":{"observed_at":"2026-08-11T21:50:58.132295Z","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":"2026-08-11T21:50:58.390259Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":"6e6b22e4-e661-4f60-a63c-2207f175ab94","year":2019},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.136496Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:02509a23cd03fbee77dc299b1a3b6c35b73ff90278b3b2146dd37311a7d42ddf","observation_id":"57571e39-e7ab-4996-b57c-03adf50dccb2","resolution":{"observed_at":"2026-08-11T21:50:58.396526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T21:50:58.140960Z","title":"Adam: A Method for Stochastic Optimization,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.140960Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:ccf4948ba90463b0867ad2e0cd224164153ab14f085d0253b3d2e56f88916445","observation_id":"54c57ab3-6f90-4f18-9683-a98f82140b74","resolution":{"observed_at":"2026-08-11T21:50:58.140960Z","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":"2026-08-11T21:50:58.145156Z","title":"Shufflenet: An extremely efficient convolutional neural network for mobile devices,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.145156Z"},"links":{"citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:9707a0330038fcea85d3a5025ad0d5281e53d5b04a68ff68c87bb3bd4b9b31ee","observation_id":"3e512f13-1771-4066-aebb-99b267c0581c","resolution":{"observed_at":"2026-08-11T21:50:58.145156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04861","last_updated":"2017-04-17T03:57:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-04-17T03:57:34Z","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.04861","snapshot_observed_at":"2026-08-11T21:50:58.149123Z","title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:58.149123Z"},"links":{"cited_paper":"/paper/1704.04861","citing_paper":"/paper/2412.04521"},"observation_digest":"sha256:8b9a398bda6e1b57afed38a3d3fea760be01b59909ed44bf846efc744d462ce1","observation_id":"73e4a6de-346e-4bfb-a830-f5b2a5b66d40","resolution":{"observed_at":"2026-08-11T21:50:58.149123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.04521","last_updated":"2024-12-05T12:32:40Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T16:24:01.824754Z","submitted_at":"2024-12-05T12:32:40Z","title":"FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":27},"total_outbound_references":45},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2412.04521."}