{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ROK6VLHN4YDIGOURPQH5CEFBXC","short_pith_number":"pith:ROK6VLHN","canonical_record":{"source":{"id":"2209.02257","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-06T07:09:21Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"4fee7f7e75ea9752825293208ca6f67d26569820b5addeabcc27885ea617c6e9","abstract_canon_sha256":"81b6b72f665bfa88e387bdeb44318148af2f21a85d7b6da4599cfbb5e5df551f"},"schema_version":"1.0"},"canonical_sha256":"8b95eaacede606833a917c0fd110a1b893ba8a7ba944015d1f64e22e07590d43","source":{"kind":"arxiv","id":"2209.02257","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.02257","created_at":"2026-07-05T06:12:36Z"},{"alias_kind":"arxiv_version","alias_value":"2209.02257v2","created_at":"2026-07-05T06:12:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.02257","created_at":"2026-07-05T06:12:36Z"},{"alias_kind":"pith_short_12","alias_value":"ROK6VLHN4YDI","created_at":"2026-07-05T06:12:36Z"},{"alias_kind":"pith_short_16","alias_value":"ROK6VLHN4YDIGOUR","created_at":"2026-07-05T06:12:36Z"},{"alias_kind":"pith_short_8","alias_value":"ROK6VLHN","created_at":"2026-07-05T06:12:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ROK6VLHN4YDIGOURPQH5CEFBXC","target":"record","payload":{"canonical_record":{"source":{"id":"2209.02257","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-06T07:09:21Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"4fee7f7e75ea9752825293208ca6f67d26569820b5addeabcc27885ea617c6e9","abstract_canon_sha256":"81b6b72f665bfa88e387bdeb44318148af2f21a85d7b6da4599cfbb5e5df551f"},"schema_version":"1.0"},"canonical_sha256":"8b95eaacede606833a917c0fd110a1b893ba8a7ba944015d1f64e22e07590d43","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:12:36.066546Z","signature_b64":"Lw0zWzqm91rUXGjQfshmoZzc3LJRh569MwMUFeeXT9WcNGCXSg+GpbyBVVmQFj26b09UJV9BfoqKcYKAxvX0Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b95eaacede606833a917c0fd110a1b893ba8a7ba944015d1f64e22e07590d43","last_reissued_at":"2026-07-05T06:12:36.066115Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:12:36.066115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.02257","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:12:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kIR8GELKNEhdIzTZAyI7iyEfDu7/GLg+L9USP2AmMDHaNL14tAfm7zANeXv9+uXsGGxU6wN1nv8aSnujUIhcBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:03:55.921267Z"},"content_sha256":"51cb371be16382a695d56aa54e5002a41bc31612bad5bca3661869156533c059","schema_version":"1.0","event_id":"sha256:51cb371be16382a695d56aa54e5002a41bc31612bad5bca3661869156533c059"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ROK6VLHN4YDIGOURPQH5CEFBXC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Faster federated optimization under second-order similarity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ahmed Khaled, Chi Jin","submitted_at":"2022-09-06T07:09:21Z","abstract_excerpt":"Federated learning (FL) is a subfield of machine learning where multiple clients try to collaboratively learn a model over a network under communication constraints. We consider finite-sum federated optimization under a second-order function similarity condition and strong convexity, and propose two new algorithms: SVRP and Catalyzed SVRP. This second-order similarity condition has grown popular recently, and is satisfied in many applications including distributed statistical learning and differentially private empirical risk minimization. The first algorithm, SVRP, combines approximate stocha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.02257","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/2209.02257/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":"2026-07-05T06:12:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7WXnMLYhLjhex68Df7ERqFpEt1w81e3hkhfcdaJ0KRAAtdgzL5v/og5awMKjo0esrjk/fbitgpFu/t4HhXPBBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:03:55.921806Z"},"content_sha256":"7ede74bd603ff2d3080a8dece72a2aaf71a6c0b4ab378f7df0a625b30ff6076f","schema_version":"1.0","event_id":"sha256:7ede74bd603ff2d3080a8dece72a2aaf71a6c0b4ab378f7df0a625b30ff6076f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ROK6VLHN4YDIGOURPQH5CEFBXC/bundle.json","state_url":"https://pith.science/pith/ROK6VLHN4YDIGOURPQH5CEFBXC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ROK6VLHN4YDIGOURPQH5CEFBXC/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":"2026-08-05T03:03:55Z","links":{"resolver":"https://pith.science/pith/ROK6VLHN4YDIGOURPQH5CEFBXC","bundle":"https://pith.science/pith/ROK6VLHN4YDIGOURPQH5CEFBXC/bundle.json","state":"https://pith.science/pith/ROK6VLHN4YDIGOURPQH5CEFBXC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ROK6VLHN4YDIGOURPQH5CEFBXC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ROK6VLHN4YDIGOURPQH5CEFBXC","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":"81b6b72f665bfa88e387bdeb44318148af2f21a85d7b6da4599cfbb5e5df551f","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-06T07:09:21Z","title_canon_sha256":"4fee7f7e75ea9752825293208ca6f67d26569820b5addeabcc27885ea617c6e9"},"schema_version":"1.0","source":{"id":"2209.02257","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.02257","created_at":"2026-07-05T06:12:36Z"},{"alias_kind":"arxiv_version","alias_value":"2209.02257v2","created_at":"2026-07-05T06:12:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.02257","created_at":"2026-07-05T06:12:36Z"},{"alias_kind":"pith_short_12","alias_value":"ROK6VLHN4YDI","created_at":"2026-07-05T06:12:36Z"},{"alias_kind":"pith_short_16","alias_value":"ROK6VLHN4YDIGOUR","created_at":"2026-07-05T06:12:36Z"},{"alias_kind":"pith_short_8","alias_value":"ROK6VLHN","created_at":"2026-07-05T06:12:36Z"}],"graph_snapshots":[{"event_id":"sha256:7ede74bd603ff2d3080a8dece72a2aaf71a6c0b4ab378f7df0a625b30ff6076f","target":"graph","created_at":"2026-07-05T06:12:36Z","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/2209.02257/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning (FL) is a subfield of machine learning where multiple clients try to collaboratively learn a model over a network under communication constraints. We consider finite-sum federated optimization under a second-order function similarity condition and strong convexity, and propose two new algorithms: SVRP and Catalyzed SVRP. This second-order similarity condition has grown popular recently, and is satisfied in many applications including distributed statistical learning and differentially private empirical risk minimization. The first algorithm, SVRP, combines approximate stocha","authors_text":"Ahmed Khaled, Chi Jin","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-06T07:09:21Z","title":"Faster federated optimization under second-order similarity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.02257","kind":"arxiv","version":2},"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":"sha256:51cb371be16382a695d56aa54e5002a41bc31612bad5bca3661869156533c059","target":"record","created_at":"2026-07-05T06:12:36Z","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":"81b6b72f665bfa88e387bdeb44318148af2f21a85d7b6da4599cfbb5e5df551f","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-06T07:09:21Z","title_canon_sha256":"4fee7f7e75ea9752825293208ca6f67d26569820b5addeabcc27885ea617c6e9"},"schema_version":"1.0","source":{"id":"2209.02257","kind":"arxiv","version":2}},"canonical_sha256":"8b95eaacede606833a917c0fd110a1b893ba8a7ba944015d1f64e22e07590d43","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8b95eaacede606833a917c0fd110a1b893ba8a7ba944015d1f64e22e07590d43","first_computed_at":"2026-07-05T06:12:36.066115Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:12:36.066115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Lw0zWzqm91rUXGjQfshmoZzc3LJRh569MwMUFeeXT9WcNGCXSg+GpbyBVVmQFj26b09UJV9BfoqKcYKAxvX0Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:12:36.066546Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.02257","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:51cb371be16382a695d56aa54e5002a41bc31612bad5bca3661869156533c059","sha256:7ede74bd603ff2d3080a8dece72a2aaf71a6c0b4ab378f7df0a625b30ff6076f"],"state_sha256":"c4d434db812fb21174a0ae49d54033acda41de57c23d2860844e710c8d0ff340"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ijuAJnaARQ4IaLL3pm9o3fjtwlH3ND8MkH5VT0OC7aEwWUPHoHyJLQJuvrFgxd4BIUHBYCLUeThQgsLWIcM+Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T03:03:55.927981Z","bundle_sha256":"74ac7b537e64a675952036bf7383384b5bf948e9bc48627c4d30c5677948195e"}}