{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KFEP6YYFMSRT5KCHBPCRKNL4YQ","short_pith_number":"pith:KFEP6YYF","schema_version":"1.0","canonical_sha256":"5148ff630564a33ea8470bc515357cc40a150a5d7097b55df80e8ec3ecad2885","source":{"kind":"arxiv","id":"2209.05148","version":1},"attestation_state":"computed","paper":{"title":"Personalized Federated Learning with Communication Compression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DC","math.OC"],"primary_cat":"cs.LG","authors_text":"Aritra Dutta, El houcine Bergou, Konstantin Burlachenko, Peter Richt\\'arik","submitted_at":"2022-09-12T11:08:44Z","abstract_excerpt":"In contrast to training traditional machine learning (ML) models in data centers, federated learning (FL) trains ML models over local datasets contained on resource-constrained heterogeneous edge devices. Existing FL algorithms aim to learn a single global model for all participating devices, which may not be helpful to all devices participating in the training due to the heterogeneity of the data across the devices. Recently, Hanzely and Richt\\'{a}rik (2020) proposed a new formulation for training personalized FL models aimed at balancing the trade-off between the traditional global model and"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2209.05148","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-12T11:08:44Z","cross_cats_sorted":["cs.AI","cs.DC","math.OC"],"title_canon_sha256":"d21d519bb0ebbe637af8e12b6fa43bcc32e6af5d4e05e87f8821242ed79fe605","abstract_canon_sha256":"5a043c4d5eea42c39d3d8973507cdee822b094e94b8e46a7c7fc943ac433033c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:56:27.424179Z","signature_b64":"Eui8GKMUyCMfT0Jolz8ZtNb6VZPDdcTolKtFhuq9cAAyvMmFgbHnMkdPQVg4HW340qbkvNKVZJrRF9Qqwv01DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5148ff630564a33ea8470bc515357cc40a150a5d7097b55df80e8ec3ecad2885","last_reissued_at":"2026-07-05T04:56:27.423817Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:56:27.423817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Personalized Federated Learning with Communication Compression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DC","math.OC"],"primary_cat":"cs.LG","authors_text":"Aritra Dutta, El houcine Bergou, Konstantin Burlachenko, Peter Richt\\'arik","submitted_at":"2022-09-12T11:08:44Z","abstract_excerpt":"In contrast to training traditional machine learning (ML) models in data centers, federated learning (FL) trains ML models over local datasets contained on resource-constrained heterogeneous edge devices. Existing FL algorithms aim to learn a single global model for all participating devices, which may not be helpful to all devices participating in the training due to the heterogeneity of the data across the devices. Recently, Hanzely and Richt\\'{a}rik (2020) proposed a new formulation for training personalized FL models aimed at balancing the trade-off between the traditional global model and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.05148","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2209.05148/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2209.05148","created_at":"2026-07-05T04:56:27.423872+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.05148v1","created_at":"2026-07-05T04:56:27.423872+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.05148","created_at":"2026-07-05T04:56:27.423872+00:00"},{"alias_kind":"pith_short_12","alias_value":"KFEP6YYFMSRT","created_at":"2026-07-05T04:56:27.423872+00:00"},{"alias_kind":"pith_short_16","alias_value":"KFEP6YYFMSRT5KCH","created_at":"2026-07-05T04:56:27.423872+00:00"},{"alias_kind":"pith_short_8","alias_value":"KFEP6YYF","created_at":"2026-07-05T04:56:27.423872+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KFEP6YYFMSRT5KCHBPCRKNL4YQ","json":"https://pith.science/pith/KFEP6YYFMSRT5KCHBPCRKNL4YQ.json","graph_json":"https://pith.science/api/pith-number/KFEP6YYFMSRT5KCHBPCRKNL4YQ/graph.json","events_json":"https://pith.science/api/pith-number/KFEP6YYFMSRT5KCHBPCRKNL4YQ/events.json","paper":"https://pith.science/paper/KFEP6YYF"},"agent_actions":{"view_html":"https://pith.science/pith/KFEP6YYFMSRT5KCHBPCRKNL4YQ","download_json":"https://pith.science/pith/KFEP6YYFMSRT5KCHBPCRKNL4YQ.json","view_paper":"https://pith.science/paper/KFEP6YYF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.05148&json=true","fetch_graph":"https://pith.science/api/pith-number/KFEP6YYFMSRT5KCHBPCRKNL4YQ/graph.json","fetch_events":"https://pith.science/api/pith-number/KFEP6YYFMSRT5KCHBPCRKNL4YQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KFEP6YYFMSRT5KCHBPCRKNL4YQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KFEP6YYFMSRT5KCHBPCRKNL4YQ/action/storage_attestation","attest_author":"https://pith.science/pith/KFEP6YYFMSRT5KCHBPCRKNL4YQ/action/author_attestation","sign_citation":"https://pith.science/pith/KFEP6YYFMSRT5KCHBPCRKNL4YQ/action/citation_signature","submit_replication":"https://pith.science/pith/KFEP6YYFMSRT5KCHBPCRKNL4YQ/action/replication_record"}},"created_at":"2026-07-05T04:56:27.423872+00:00","updated_at":"2026-07-05T04:56:27.423872+00:00"}