{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3HDX3XRJ34CGRMFPW2GKFSAU6U","short_pith_number":"pith:3HDX3XRJ","schema_version":"1.0","canonical_sha256":"d9c77dde29df0468b0afb68ca2c814f51fc8e129b12744352617514d5ea586cd","source":{"kind":"arxiv","id":"2303.13363","version":1},"attestation_state":"computed","paper":{"title":"FS-Real: Towards Real-World Cross-Device Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bolin Ding, Daoyuan Chen, Dawei Gao, Jingren Zhou, Xuchen Pan, Yaliang Li, Yuexiang Xie, Zitao Li","submitted_at":"2023-03-23T15:37:17Z","abstract_excerpt":"Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in both academia and industry. However, there is still a considerable gap between the flourishing FL research and real-world scenarios, mainly caused by the characteristics of heterogeneous devices and its scales. Most existing works conduct evaluations with homogeneous devices, which are mismatched with the diversity and variability of heterogeneous devices in real-world scenarios. Moreover, it is challenging to conduct r"},"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":"2303.13363","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-23T15:37:17Z","cross_cats_sorted":[],"title_canon_sha256":"0945bc20bf8f75646e1a5337ee7d025dbe64052317e0f85bedb043afd5f1d797","abstract_canon_sha256":"7452a5abee2493e2153f8116cd4b8e3bb97a0f54fa7dd21fb89bf2c84a762e0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:54:01.172873Z","signature_b64":"TxBHGoeqGMk5rFISM8McGknjhaE5gnp+thFklvIt9GIMxa9mGNzXQB4M4Fb6UcKbLWZAWkOvltjB4PMK+L8JCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9c77dde29df0468b0afb68ca2c814f51fc8e129b12744352617514d5ea586cd","last_reissued_at":"2026-07-05T05:54:01.172453Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:54:01.172453Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FS-Real: Towards Real-World Cross-Device Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bolin Ding, Daoyuan Chen, Dawei Gao, Jingren Zhou, Xuchen Pan, Yaliang Li, Yuexiang Xie, Zitao Li","submitted_at":"2023-03-23T15:37:17Z","abstract_excerpt":"Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in both academia and industry. However, there is still a considerable gap between the flourishing FL research and real-world scenarios, mainly caused by the characteristics of heterogeneous devices and its scales. Most existing works conduct evaluations with homogeneous devices, which are mismatched with the diversity and variability of heterogeneous devices in real-world scenarios. Moreover, it is challenging to conduct r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.13363","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/2303.13363/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":"2303.13363","created_at":"2026-07-05T05:54:01.172510+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.13363v1","created_at":"2026-07-05T05:54:01.172510+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.13363","created_at":"2026-07-05T05:54:01.172510+00:00"},{"alias_kind":"pith_short_12","alias_value":"3HDX3XRJ34CG","created_at":"2026-07-05T05:54:01.172510+00:00"},{"alias_kind":"pith_short_16","alias_value":"3HDX3XRJ34CGRMFP","created_at":"2026-07-05T05:54:01.172510+00:00"},{"alias_kind":"pith_short_8","alias_value":"3HDX3XRJ","created_at":"2026-07-05T05:54:01.172510+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.13434","citing_title":"Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity","ref_index":104,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3HDX3XRJ34CGRMFPW2GKFSAU6U","json":"https://pith.science/pith/3HDX3XRJ34CGRMFPW2GKFSAU6U.json","graph_json":"https://pith.science/api/pith-number/3HDX3XRJ34CGRMFPW2GKFSAU6U/graph.json","events_json":"https://pith.science/api/pith-number/3HDX3XRJ34CGRMFPW2GKFSAU6U/events.json","paper":"https://pith.science/paper/3HDX3XRJ"},"agent_actions":{"view_html":"https://pith.science/pith/3HDX3XRJ34CGRMFPW2GKFSAU6U","download_json":"https://pith.science/pith/3HDX3XRJ34CGRMFPW2GKFSAU6U.json","view_paper":"https://pith.science/paper/3HDX3XRJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.13363&json=true","fetch_graph":"https://pith.science/api/pith-number/3HDX3XRJ34CGRMFPW2GKFSAU6U/graph.json","fetch_events":"https://pith.science/api/pith-number/3HDX3XRJ34CGRMFPW2GKFSAU6U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3HDX3XRJ34CGRMFPW2GKFSAU6U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3HDX3XRJ34CGRMFPW2GKFSAU6U/action/storage_attestation","attest_author":"https://pith.science/pith/3HDX3XRJ34CGRMFPW2GKFSAU6U/action/author_attestation","sign_citation":"https://pith.science/pith/3HDX3XRJ34CGRMFPW2GKFSAU6U/action/citation_signature","submit_replication":"https://pith.science/pith/3HDX3XRJ34CGRMFPW2GKFSAU6U/action/replication_record"}},"created_at":"2026-07-05T05:54:01.172510+00:00","updated_at":"2026-07-05T05:54:01.172510+00:00"}