{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:AGSUC75GIJSOG6XPFGKLNGLZ7A","short_pith_number":"pith:AGSUC75G","schema_version":"1.0","canonical_sha256":"01a5417fa64264e37aef2994b69979f826561856e5eeac0b11da4663330ee56d","source":{"kind":"arxiv","id":"2006.10672","version":2},"attestation_state":"computed","paper":{"title":"Federated Learning With Quantized Global Model Updates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Deniz Gunduz, H. Vincent Poor, Mohammad Mohammadi Amiri, Sanjeev R. Kulkarni","submitted_at":"2020-06-18T16:55:20Z","abstract_excerpt":"We study federated learning (FL), which enables mobile devices to utilize their local datasets to collaboratively train a global model with the help of a central server, while keeping data localized. At each iteration, the server broadcasts the current global model to the devices for local training, and aggregates the local model updates from the devices to update the global model. Previous work on the communication efficiency of FL has mainly focused on the aggregation of model updates from the devices, assuming perfect broadcasting of the global model. In this paper, we instead consider broa"},"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":"2006.10672","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2020-06-18T16:55:20Z","cross_cats_sorted":["cs.DC","cs.LG","math.IT"],"title_canon_sha256":"7c8d77161c7add031a4bf427b767537bbf5db24c9091b84b7170c210339f2334","abstract_canon_sha256":"ca02d07c5e51ce42a642bde3ac11b806b437df9fc9a1d4d23277d756bb36d23c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:40:57.195194Z","signature_b64":"/MppvEYmGGX5hEo5kVk4LuETMJ/F1/JLjWFm62NrqNVczTmVccOLBapv0b4ObNqvTcNU4id//ZgVdi6DnoKnBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01a5417fa64264e37aef2994b69979f826561856e5eeac0b11da4663330ee56d","last_reissued_at":"2026-07-05T01:40:57.194716Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:40:57.194716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Learning With Quantized Global Model Updates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Deniz Gunduz, H. Vincent Poor, Mohammad Mohammadi Amiri, Sanjeev R. Kulkarni","submitted_at":"2020-06-18T16:55:20Z","abstract_excerpt":"We study federated learning (FL), which enables mobile devices to utilize their local datasets to collaboratively train a global model with the help of a central server, while keeping data localized. At each iteration, the server broadcasts the current global model to the devices for local training, and aggregates the local model updates from the devices to update the global model. Previous work on the communication efficiency of FL has mainly focused on the aggregation of model updates from the devices, assuming perfect broadcasting of the global model. In this paper, we instead consider broa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.10672","kind":"arxiv","version":2},"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/2006.10672/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":"2006.10672","created_at":"2026-07-05T01:40:57.194773+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.10672v2","created_at":"2026-07-05T01:40:57.194773+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.10672","created_at":"2026-07-05T01:40:57.194773+00:00"},{"alias_kind":"pith_short_12","alias_value":"AGSUC75GIJSO","created_at":"2026-07-05T01:40:57.194773+00:00"},{"alias_kind":"pith_short_16","alias_value":"AGSUC75GIJSOG6XP","created_at":"2026-07-05T01:40:57.194773+00:00"},{"alias_kind":"pith_short_8","alias_value":"AGSUC75G","created_at":"2026-07-05T01:40:57.194773+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26822","citing_title":"Quantization in Federated Learning: Methods, Challenges and Future Directions","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2501.12709","citing_title":"Experimentally validated quantum-secure federated learning over a multi-user quantum network","ref_index":85,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AGSUC75GIJSOG6XPFGKLNGLZ7A","json":"https://pith.science/pith/AGSUC75GIJSOG6XPFGKLNGLZ7A.json","graph_json":"https://pith.science/api/pith-number/AGSUC75GIJSOG6XPFGKLNGLZ7A/graph.json","events_json":"https://pith.science/api/pith-number/AGSUC75GIJSOG6XPFGKLNGLZ7A/events.json","paper":"https://pith.science/paper/AGSUC75G"},"agent_actions":{"view_html":"https://pith.science/pith/AGSUC75GIJSOG6XPFGKLNGLZ7A","download_json":"https://pith.science/pith/AGSUC75GIJSOG6XPFGKLNGLZ7A.json","view_paper":"https://pith.science/paper/AGSUC75G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.10672&json=true","fetch_graph":"https://pith.science/api/pith-number/AGSUC75GIJSOG6XPFGKLNGLZ7A/graph.json","fetch_events":"https://pith.science/api/pith-number/AGSUC75GIJSOG6XPFGKLNGLZ7A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AGSUC75GIJSOG6XPFGKLNGLZ7A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AGSUC75GIJSOG6XPFGKLNGLZ7A/action/storage_attestation","attest_author":"https://pith.science/pith/AGSUC75GIJSOG6XPFGKLNGLZ7A/action/author_attestation","sign_citation":"https://pith.science/pith/AGSUC75GIJSOG6XPFGKLNGLZ7A/action/citation_signature","submit_replication":"https://pith.science/pith/AGSUC75GIJSOG6XPFGKLNGLZ7A/action/replication_record"}},"created_at":"2026-07-05T01:40:57.194773+00:00","updated_at":"2026-07-05T01:40:57.194773+00:00"}