{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:OPRDJGJO2O2VA73Y7WWSSVT2KK","short_pith_number":"pith:OPRDJGJO","schema_version":"1.0","canonical_sha256":"73e234992ed3b5507f78fdad29567a52a7686931eff99f4207ed7e50d64cfe43","source":{"kind":"arxiv","id":"2006.06983","version":4},"attestation_state":"computed","paper":{"title":"Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chengxu Yang, Kaigui Bian, Mengwei Xu, Qipeng Wang, Xuanzhe Liu, Yunxin Liu, Zhenpeng Chen","submitted_at":"2020-06-12T07:49:21Z","abstract_excerpt":"Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is heterogeneity, which resides in the various hardware specifications and dynamic states across the participating devices. Theoretically, heterogeneity can exert a huge influence on the FL training process, e.g., causing a device unavailable for training or unable to upload its model updates. Unfortunately, these impacts have never been systematically studied and quantified in existing FL literature.\n  In this paper, we"},"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":"2006.06983","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-12T07:49:21Z","cross_cats_sorted":["cs.DC","stat.ML"],"title_canon_sha256":"48bae8086cb0f414f10c8dcb8b107f9920882a1037aeed527834291e6d359078","abstract_canon_sha256":"c1ad35a4de2614c02ed80b8c1ea870f80a5d8aeaf7fb4e6046b32d484cb76741"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:22:22.660887Z","signature_b64":"QxdEA7kzRdH6NkjrR6wR7HiqFHaqEk649S2hFWVLjpLlSDrb1vG9aYpk0xEII6Fidqa4MYD+JgvXebMqGGSyCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73e234992ed3b5507f78fdad29567a52a7686931eff99f4207ed7e50d64cfe43","last_reissued_at":"2026-07-05T02:22:22.660438Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:22:22.660438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chengxu Yang, Kaigui Bian, Mengwei Xu, Qipeng Wang, Xuanzhe Liu, Yunxin Liu, Zhenpeng Chen","submitted_at":"2020-06-12T07:49:21Z","abstract_excerpt":"Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is heterogeneity, which resides in the various hardware specifications and dynamic states across the participating devices. Theoretically, heterogeneity can exert a huge influence on the FL training process, e.g., causing a device unavailable for training or unable to upload its model updates. Unfortunately, these impacts have never been systematically studied and quantified in existing FL literature.\n  In this paper, we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.06983","kind":"arxiv","version":4},"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/2006.06983/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":"2006.06983","created_at":"2026-07-05T02:22:22.660498+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.06983v4","created_at":"2026-07-05T02:22:22.660498+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.06983","created_at":"2026-07-05T02:22:22.660498+00:00"},{"alias_kind":"pith_short_12","alias_value":"OPRDJGJO2O2V","created_at":"2026-07-05T02:22:22.660498+00:00"},{"alias_kind":"pith_short_16","alias_value":"OPRDJGJO2O2VA73Y","created_at":"2026-07-05T02:22:22.660498+00:00"},{"alias_kind":"pith_short_8","alias_value":"OPRDJGJO","created_at":"2026-07-05T02:22:22.660498+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":99,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OPRDJGJO2O2VA73Y7WWSSVT2KK","json":"https://pith.science/pith/OPRDJGJO2O2VA73Y7WWSSVT2KK.json","graph_json":"https://pith.science/api/pith-number/OPRDJGJO2O2VA73Y7WWSSVT2KK/graph.json","events_json":"https://pith.science/api/pith-number/OPRDJGJO2O2VA73Y7WWSSVT2KK/events.json","paper":"https://pith.science/paper/OPRDJGJO"},"agent_actions":{"view_html":"https://pith.science/pith/OPRDJGJO2O2VA73Y7WWSSVT2KK","download_json":"https://pith.science/pith/OPRDJGJO2O2VA73Y7WWSSVT2KK.json","view_paper":"https://pith.science/paper/OPRDJGJO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.06983&json=true","fetch_graph":"https://pith.science/api/pith-number/OPRDJGJO2O2VA73Y7WWSSVT2KK/graph.json","fetch_events":"https://pith.science/api/pith-number/OPRDJGJO2O2VA73Y7WWSSVT2KK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OPRDJGJO2O2VA73Y7WWSSVT2KK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OPRDJGJO2O2VA73Y7WWSSVT2KK/action/storage_attestation","attest_author":"https://pith.science/pith/OPRDJGJO2O2VA73Y7WWSSVT2KK/action/author_attestation","sign_citation":"https://pith.science/pith/OPRDJGJO2O2VA73Y7WWSSVT2KK/action/citation_signature","submit_replication":"https://pith.science/pith/OPRDJGJO2O2VA73Y7WWSSVT2KK/action/replication_record"}},"created_at":"2026-07-05T02:22:22.660498+00:00","updated_at":"2026-07-05T02:22:22.660498+00:00"}