{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:CBIJHCQLIONENMVKF25U373TBQ","short_pith_number":"pith:CBIJHCQL","canonical_record":{"source":{"id":"2506.07159","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-06-08T14:09:47Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d5d1fe5ff0073aade3c6dcd7dc647dc77d44d257f4b577e2859b7864998bdf39","abstract_canon_sha256":"48ae27cc16f9625bf645003d00f7fa07e346e75d4e62db993518e8b39789c55e"},"schema_version":"1.0"},"canonical_sha256":"1050938a0b439a46b2aa2ebb4dff730c3eab6d79fca61f6ffbdf324213094f5d","source":{"kind":"arxiv","id":"2506.07159","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07159","created_at":"2026-07-05T11:18:02Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07159v1","created_at":"2026-07-05T11:18:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07159","created_at":"2026-07-05T11:18:02Z"},{"alias_kind":"pith_short_12","alias_value":"CBIJHCQLIONE","created_at":"2026-07-05T11:18:02Z"},{"alias_kind":"pith_short_16","alias_value":"CBIJHCQLIONENMVK","created_at":"2026-07-05T11:18:02Z"},{"alias_kind":"pith_short_8","alias_value":"CBIJHCQL","created_at":"2026-07-05T11:18:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:CBIJHCQLIONENMVKF25U373TBQ","target":"record","payload":{"canonical_record":{"source":{"id":"2506.07159","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-06-08T14:09:47Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d5d1fe5ff0073aade3c6dcd7dc647dc77d44d257f4b577e2859b7864998bdf39","abstract_canon_sha256":"48ae27cc16f9625bf645003d00f7fa07e346e75d4e62db993518e8b39789c55e"},"schema_version":"1.0"},"canonical_sha256":"1050938a0b439a46b2aa2ebb4dff730c3eab6d79fca61f6ffbdf324213094f5d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:02.738408Z","signature_b64":"RF8zWOK5KWfUOVBf3HqwUuDlWMnl5Zb60jXob7No+hHGuXBnu8Q5YxDuPTbyBE6swvTraxhSci8ds9aesEuZDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1050938a0b439a46b2aa2ebb4dff730c3eab6d79fca61f6ffbdf324213094f5d","last_reissued_at":"2026-07-05T11:18:02.737983Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:02.737983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.07159","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":"2026-07-05T11:18:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"20sAHTuxEckiY3TFVPfOG/Je3w7o4AMfEWZZjglRlpkOmOtRg4ZcM55iqTNve2dEC8zcgqaRVA46pabej5DkBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:51:49.283635Z"},"content_sha256":"10f049ab886b141c48784c531a747b7efffbde02ecf39dcb440abea8eed33a76","schema_version":"1.0","event_id":"sha256:10f049ab886b141c48784c531a747b7efffbde02ecf39dcb440abea8eed33a76"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:CBIJHCQLIONENMVKF25U373TBQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Chalavadi Krishna Mohan, Mrinmay Sen","submitted_at":"2025-06-08T14:09:47Z","abstract_excerpt":"Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models tailored to their individual objectives, addressing the challenge of model generalization in traditional Federated Learning (FL) due to high data heterogeneity. However, existing PFL methods often require increased communication rounds to achieve the desired performance, primarily due to slow training caused by the use of first-order optimization, which has linear convergence. Additionally, many of these methods increase local computation because of the additional data fed into the model during t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07159","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/2506.07159/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":"2026-07-05T11:18:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YYKWifSa8vaGWN1nVYDqiEefgcPcypHmDvbbQlm18B8KskJ/e8P96ypvqwZxKaO/7/xBiMRQhnVG5V0khpvHCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:51:49.284252Z"},"content_sha256":"ae7d06c4954e6eb969f837e371f3d897b795fc8acdd69de001287fbc5a27a221","schema_version":"1.0","event_id":"sha256:ae7d06c4954e6eb969f837e371f3d897b795fc8acdd69de001287fbc5a27a221"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CBIJHCQLIONENMVKF25U373TBQ/bundle.json","state_url":"https://pith.science/pith/CBIJHCQLIONENMVKF25U373TBQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CBIJHCQLIONENMVKF25U373TBQ/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":"2026-08-07T23:51:49Z","links":{"resolver":"https://pith.science/pith/CBIJHCQLIONENMVKF25U373TBQ","bundle":"https://pith.science/pith/CBIJHCQLIONENMVKF25U373TBQ/bundle.json","state":"https://pith.science/pith/CBIJHCQLIONENMVKF25U373TBQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CBIJHCQLIONENMVKF25U373TBQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CBIJHCQLIONENMVKF25U373TBQ","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":"48ae27cc16f9625bf645003d00f7fa07e346e75d4e62db993518e8b39789c55e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-06-08T14:09:47Z","title_canon_sha256":"d5d1fe5ff0073aade3c6dcd7dc647dc77d44d257f4b577e2859b7864998bdf39"},"schema_version":"1.0","source":{"id":"2506.07159","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07159","created_at":"2026-07-05T11:18:02Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07159v1","created_at":"2026-07-05T11:18:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07159","created_at":"2026-07-05T11:18:02Z"},{"alias_kind":"pith_short_12","alias_value":"CBIJHCQLIONE","created_at":"2026-07-05T11:18:02Z"},{"alias_kind":"pith_short_16","alias_value":"CBIJHCQLIONENMVK","created_at":"2026-07-05T11:18:02Z"},{"alias_kind":"pith_short_8","alias_value":"CBIJHCQL","created_at":"2026-07-05T11:18:02Z"}],"graph_snapshots":[{"event_id":"sha256:ae7d06c4954e6eb969f837e371f3d897b795fc8acdd69de001287fbc5a27a221","target":"graph","created_at":"2026-07-05T11:18:02Z","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/2506.07159/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models tailored to their individual objectives, addressing the challenge of model generalization in traditional Federated Learning (FL) due to high data heterogeneity. However, existing PFL methods often require increased communication rounds to achieve the desired performance, primarily due to slow training caused by the use of first-order optimization, which has linear convergence. Additionally, many of these methods increase local computation because of the additional data fed into the model during t","authors_text":"Chalavadi Krishna Mohan, Mrinmay Sen","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-06-08T14:09:47Z","title":"pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07159","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":"sha256:10f049ab886b141c48784c531a747b7efffbde02ecf39dcb440abea8eed33a76","target":"record","created_at":"2026-07-05T11:18:02Z","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":"48ae27cc16f9625bf645003d00f7fa07e346e75d4e62db993518e8b39789c55e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-06-08T14:09:47Z","title_canon_sha256":"d5d1fe5ff0073aade3c6dcd7dc647dc77d44d257f4b577e2859b7864998bdf39"},"schema_version":"1.0","source":{"id":"2506.07159","kind":"arxiv","version":1}},"canonical_sha256":"1050938a0b439a46b2aa2ebb4dff730c3eab6d79fca61f6ffbdf324213094f5d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1050938a0b439a46b2aa2ebb4dff730c3eab6d79fca61f6ffbdf324213094f5d","first_computed_at":"2026-07-05T11:18:02.737983Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:02.737983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RF8zWOK5KWfUOVBf3HqwUuDlWMnl5Zb60jXob7No+hHGuXBnu8Q5YxDuPTbyBE6swvTraxhSci8ds9aesEuZDA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:02.738408Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.07159","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:10f049ab886b141c48784c531a747b7efffbde02ecf39dcb440abea8eed33a76","sha256:ae7d06c4954e6eb969f837e371f3d897b795fc8acdd69de001287fbc5a27a221"],"state_sha256":"20284d6c77a97313bc2d6674ec0f70d559548482d197dfd2ba68de77031fefe0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vs0OlxsmjSj2CHMPRUYFC0WsFPniQHYKLwtgScYFCyvS/IjBHq8NX9srzWth16TLM6T918ooL8BnU/MmT7BwAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T23:51:49.290441Z","bundle_sha256":"c6aef9bfbce89aa89bb9401a8a042e5df2c4c7b9a186eafcaeb41761a555811f"}}