{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ZID3277BZNNDNUFB2VD6MBW62L","short_pith_number":"pith:ZID3277B","schema_version":"1.0","canonical_sha256":"ca07bd7fe1cb5a36d0a1d547e606ded2f1c2e0179cf0e386bc1b86dfaf45eaf9","source":{"kind":"arxiv","id":"2110.14578","version":2},"attestation_state":"computed","paper":{"title":"Spatio-Temporal Federated Learning for Massive Wireless Edge Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Chun-Hung Liu, Kai-Ten Feng, Lu wei, Yu Luo","submitted_at":"2021-10-27T16:46:45Z","abstract_excerpt":"This paper presents a novel approach to conduct highly efficient federated learning (FL) over a massive wireless edge network, where an edge server and numerous mobile devices (clients) jointly learn a global model without transporting the huge amount of data collected by the mobile devices to the edge server. The proposed FL approach is referred to as spatio-temporal FL (STFL), which jointly exploits the spatial and temporal correlations between the learning updates from different mobile devices scheduled to join STFL in various training epochs. The STFL model not only represents the realisti"},"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":"2110.14578","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-27T16:46:45Z","cross_cats_sorted":["cs.NI","eess.SP"],"title_canon_sha256":"5283ebc8733b545b4f791e9ab363e50a26e998abd005ef18f53f08ff2518bf65","abstract_canon_sha256":"08b2fa5d78c6b17d08d828b1dff7bc8d54c920ce6c5ebe502b481fa0e8a352ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:50:36.885637Z","signature_b64":"0Pu+qFZuwL378RFP1FSLHBeV61xelVi4NhB5HNdc9+ZoUtWCorQBT8KA/r750yFChDst9+6r2VUt9ORulTe8AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca07bd7fe1cb5a36d0a1d547e606ded2f1c2e0179cf0e386bc1b86dfaf45eaf9","last_reissued_at":"2026-07-05T03:50:36.885208Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:50:36.885208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatio-Temporal Federated Learning for Massive Wireless Edge Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Chun-Hung Liu, Kai-Ten Feng, Lu wei, Yu Luo","submitted_at":"2021-10-27T16:46:45Z","abstract_excerpt":"This paper presents a novel approach to conduct highly efficient federated learning (FL) over a massive wireless edge network, where an edge server and numerous mobile devices (clients) jointly learn a global model without transporting the huge amount of data collected by the mobile devices to the edge server. The proposed FL approach is referred to as spatio-temporal FL (STFL), which jointly exploits the spatial and temporal correlations between the learning updates from different mobile devices scheduled to join STFL in various training epochs. The STFL model not only represents the realisti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.14578","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/2110.14578/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":"2110.14578","created_at":"2026-07-05T03:50:36.885264+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.14578v2","created_at":"2026-07-05T03:50:36.885264+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.14578","created_at":"2026-07-05T03:50:36.885264+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZID3277BZNND","created_at":"2026-07-05T03:50:36.885264+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZID3277BZNNDNUFB","created_at":"2026-07-05T03:50:36.885264+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZID3277B","created_at":"2026-07-05T03:50:36.885264+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/ZID3277BZNNDNUFB2VD6MBW62L","json":"https://pith.science/pith/ZID3277BZNNDNUFB2VD6MBW62L.json","graph_json":"https://pith.science/api/pith-number/ZID3277BZNNDNUFB2VD6MBW62L/graph.json","events_json":"https://pith.science/api/pith-number/ZID3277BZNNDNUFB2VD6MBW62L/events.json","paper":"https://pith.science/paper/ZID3277B"},"agent_actions":{"view_html":"https://pith.science/pith/ZID3277BZNNDNUFB2VD6MBW62L","download_json":"https://pith.science/pith/ZID3277BZNNDNUFB2VD6MBW62L.json","view_paper":"https://pith.science/paper/ZID3277B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.14578&json=true","fetch_graph":"https://pith.science/api/pith-number/ZID3277BZNNDNUFB2VD6MBW62L/graph.json","fetch_events":"https://pith.science/api/pith-number/ZID3277BZNNDNUFB2VD6MBW62L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZID3277BZNNDNUFB2VD6MBW62L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZID3277BZNNDNUFB2VD6MBW62L/action/storage_attestation","attest_author":"https://pith.science/pith/ZID3277BZNNDNUFB2VD6MBW62L/action/author_attestation","sign_citation":"https://pith.science/pith/ZID3277BZNNDNUFB2VD6MBW62L/action/citation_signature","submit_replication":"https://pith.science/pith/ZID3277BZNNDNUFB2VD6MBW62L/action/replication_record"}},"created_at":"2026-07-05T03:50:36.885264+00:00","updated_at":"2026-07-05T03:50:36.885264+00:00"}