{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:FAFUKSXIRMIPFIYXPRY42RMQMU","short_pith_number":"pith:FAFUKSXI","canonical_record":{"source":{"id":"2410.01209","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-02T03:30:53Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"ec53480c212915a153796e892c2a975680d7f5e241d43dee283f07f894707464","abstract_canon_sha256":"531ef230467397b939f67dd4b246983447cfe4ae8b9b7a23b271efc826848e6f"},"schema_version":"1.0"},"canonical_sha256":"280b454ae88b10f2a3177c71cd4590650729dfb74d1e4192eabf067502ec41bf","source":{"kind":"arxiv","id":"2410.01209","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.01209","created_at":"2026-07-05T09:14:44Z"},{"alias_kind":"arxiv_version","alias_value":"2410.01209v1","created_at":"2026-07-05T09:14:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.01209","created_at":"2026-07-05T09:14:44Z"},{"alias_kind":"pith_short_12","alias_value":"FAFUKSXIRMIP","created_at":"2026-07-05T09:14:44Z"},{"alias_kind":"pith_short_16","alias_value":"FAFUKSXIRMIPFIYX","created_at":"2026-07-05T09:14:44Z"},{"alias_kind":"pith_short_8","alias_value":"FAFUKSXI","created_at":"2026-07-05T09:14:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:FAFUKSXIRMIPFIYXPRY42RMQMU","target":"record","payload":{"canonical_record":{"source":{"id":"2410.01209","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-02T03:30:53Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"ec53480c212915a153796e892c2a975680d7f5e241d43dee283f07f894707464","abstract_canon_sha256":"531ef230467397b939f67dd4b246983447cfe4ae8b9b7a23b271efc826848e6f"},"schema_version":"1.0"},"canonical_sha256":"280b454ae88b10f2a3177c71cd4590650729dfb74d1e4192eabf067502ec41bf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:14:44.766448Z","signature_b64":"UfxD65Cs3IAGzVFpKbNvyvULVcVEqgb6w33/lHJTIhynNsNe5oOCBHiDn5dc+dhwxfje4eyedaznDeYT+PJFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"280b454ae88b10f2a3177c71cd4590650729dfb74d1e4192eabf067502ec41bf","last_reissued_at":"2026-07-05T09:14:44.765924Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:14:44.765924Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.01209","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:14:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LUc7nl2YdjrQQYqHJat3hOlVFh/o046yOAgoZFkUbchvF18WEsmGVVP5S1oX76vk9YDdHD4GiJXxSXA36OPDDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:32:56.842886Z"},"content_sha256":"2528684f645c14dde47f3f071f21cef5f43224873f3d397e5af658a63f98e73b","schema_version":"1.0","event_id":"sha256:2528684f645c14dde47f3f071f21cef5f43224873f3d397e5af658a63f98e73b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:FAFUKSXIRMIPFIYXPRY42RMQMU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Debiasing Federated Learning with Correlated Client Participation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Ermin Wei, Gauri Joshi, Pranay Sharma, Zheng Xu, Zhenyu Sun, Ziyang Zhang","submitted_at":"2024-10-02T03:30:53Z","abstract_excerpt":"In cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averaging (FedAvg) is the most popular algorithm in practice. Existing analyses of FedAvg usually assume the participating clients are independently sampled in each round from a uniform distribution, which does not reflect real-world scenarios. This paper introduces a theoretical framework that models client participation in FL as a Markov chain to study optimization convergence when clients have non-uniform and correlated p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.01209","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/2410.01209/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:14:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GgCI4EsCXQXmfVYVYmHiXnZ2OQnRuUgaxwOpqR5TmwBcJHT5rFy218UWQGXOEAuLTslXQiqh9fOhv+FSBFWuDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:32:56.843378Z"},"content_sha256":"b26f8e51338a781368ce19e3eb5bd670e2452b9deb21c8bc5706e56b24469c22","schema_version":"1.0","event_id":"sha256:b26f8e51338a781368ce19e3eb5bd670e2452b9deb21c8bc5706e56b24469c22"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FAFUKSXIRMIPFIYXPRY42RMQMU/bundle.json","state_url":"https://pith.science/pith/FAFUKSXIRMIPFIYXPRY42RMQMU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FAFUKSXIRMIPFIYXPRY42RMQMU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T23:32:56Z","links":{"resolver":"https://pith.science/pith/FAFUKSXIRMIPFIYXPRY42RMQMU","bundle":"https://pith.science/pith/FAFUKSXIRMIPFIYXPRY42RMQMU/bundle.json","state":"https://pith.science/pith/FAFUKSXIRMIPFIYXPRY42RMQMU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FAFUKSXIRMIPFIYXPRY42RMQMU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FAFUKSXIRMIPFIYXPRY42RMQMU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"531ef230467397b939f67dd4b246983447cfe4ae8b9b7a23b271efc826848e6f","cross_cats_sorted":["cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-02T03:30:53Z","title_canon_sha256":"ec53480c212915a153796e892c2a975680d7f5e241d43dee283f07f894707464"},"schema_version":"1.0","source":{"id":"2410.01209","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.01209","created_at":"2026-07-05T09:14:44Z"},{"alias_kind":"arxiv_version","alias_value":"2410.01209v1","created_at":"2026-07-05T09:14:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.01209","created_at":"2026-07-05T09:14:44Z"},{"alias_kind":"pith_short_12","alias_value":"FAFUKSXIRMIP","created_at":"2026-07-05T09:14:44Z"},{"alias_kind":"pith_short_16","alias_value":"FAFUKSXIRMIPFIYX","created_at":"2026-07-05T09:14:44Z"},{"alias_kind":"pith_short_8","alias_value":"FAFUKSXI","created_at":"2026-07-05T09:14:44Z"}],"graph_snapshots":[{"event_id":"sha256:b26f8e51338a781368ce19e3eb5bd670e2452b9deb21c8bc5706e56b24469c22","target":"graph","created_at":"2026-07-05T09:14:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2410.01209/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averaging (FedAvg) is the most popular algorithm in practice. Existing analyses of FedAvg usually assume the participating clients are independently sampled in each round from a uniform distribution, which does not reflect real-world scenarios. This paper introduces a theoretical framework that models client participation in FL as a Markov chain to study optimization convergence when clients have non-uniform and correlated p","authors_text":"Ermin Wei, Gauri Joshi, Pranay Sharma, Zheng Xu, Zhenyu Sun, Ziyang Zhang","cross_cats":["cs.DC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-02T03:30:53Z","title":"Debiasing Federated Learning with Correlated Client Participation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.01209","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2528684f645c14dde47f3f071f21cef5f43224873f3d397e5af658a63f98e73b","target":"record","created_at":"2026-07-05T09:14:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"531ef230467397b939f67dd4b246983447cfe4ae8b9b7a23b271efc826848e6f","cross_cats_sorted":["cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-02T03:30:53Z","title_canon_sha256":"ec53480c212915a153796e892c2a975680d7f5e241d43dee283f07f894707464"},"schema_version":"1.0","source":{"id":"2410.01209","kind":"arxiv","version":1}},"canonical_sha256":"280b454ae88b10f2a3177c71cd4590650729dfb74d1e4192eabf067502ec41bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"280b454ae88b10f2a3177c71cd4590650729dfb74d1e4192eabf067502ec41bf","first_computed_at":"2026-07-05T09:14:44.765924Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:14:44.765924Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UfxD65Cs3IAGzVFpKbNvyvULVcVEqgb6w33/lHJTIhynNsNe5oOCBHiDn5dc+dhwxfje4eyedaznDeYT+PJFDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:14:44.766448Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.01209","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2528684f645c14dde47f3f071f21cef5f43224873f3d397e5af658a63f98e73b","sha256:b26f8e51338a781368ce19e3eb5bd670e2452b9deb21c8bc5706e56b24469c22"],"state_sha256":"b0b97e9682f6a704e42b77720e79b285c1d93d6ec624caece7d335fff42ee266"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eu3uaNePlvtd19kZ7BhK4jj0GWaE8NJExt0O8fxgpB1Upr5K73k1DAIlUiweK4v5uJGuLRRZkIeY+lxrgirRBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T23:32:56.849242Z","bundle_sha256":"7ed584bbaaadd568198c7c1e6822dabf07ae08f51a1a5c478d029983645107f7"}}