{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:IVIGANRNT2SRF4EFIULQHFF2E5","short_pith_number":"pith:IVIGANRN","canonical_record":{"source":{"id":"2403.18375","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-03-27T09:14:36Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"dfe222dfc9239eee038e6d7ecf51d8f19f09f9028f2311390dbc2974d9843d41","abstract_canon_sha256":"5dbfc10fb215872a896950e499473ca39906e3beefca54bd0d6645adfd9182de"},"schema_version":"1.0"},"canonical_sha256":"455060362d9ea512f08545170394ba2756f50e76502ec278b8cb51f41f581cd3","source":{"kind":"arxiv","id":"2403.18375","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.18375","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"arxiv_version","alias_value":"2403.18375v1","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.18375","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_12","alias_value":"IVIGANRNT2SR","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_16","alias_value":"IVIGANRNT2SRF4EF","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_8","alias_value":"IVIGANRN","created_at":"2026-07-05T08:01:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:IVIGANRNT2SRF4EFIULQHFF2E5","target":"record","payload":{"canonical_record":{"source":{"id":"2403.18375","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-03-27T09:14:36Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"dfe222dfc9239eee038e6d7ecf51d8f19f09f9028f2311390dbc2974d9843d41","abstract_canon_sha256":"5dbfc10fb215872a896950e499473ca39906e3beefca54bd0d6645adfd9182de"},"schema_version":"1.0"},"canonical_sha256":"455060362d9ea512f08545170394ba2756f50e76502ec278b8cb51f41f581cd3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:01:21.859431Z","signature_b64":"tj4LHmjsrsgfyQe7nHxzwqv+3cfmbutLJnoW6qUoEMwgXwK9rkQjZCVCemGJN7pqKDh/wrhYgOXxLKr6aC1GCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"455060362d9ea512f08545170394ba2756f50e76502ec278b8cb51f41f581cd3","last_reissued_at":"2026-07-05T08:01:21.859012Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:01:21.859012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.18375","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":"2026-07-05T08:01:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AeFCsB1YFlnDptvRbIBddA0QN6Y32llp33sHkryuI8St1ce6jZNHPR7VwxEniueuQ5ZRUnIiR2PvD7eEZwQdBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:19:15.091044Z"},"content_sha256":"f2d507df8ee458685d973f20f064d9dd53a050b05ea4fd337fb6cc281ade2214","schema_version":"1.0","event_id":"sha256:f2d507df8ee458685d973f20f064d9dd53a050b05ea4fd337fb6cc281ade2214"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:IVIGANRNT2SRF4EFIULQHFF2E5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Alejandro Cohen, Natalie Lang, Nir Shlezinger","submitted_at":"2024-03-27T09:14:36Z","abstract_excerpt":"Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN) models in parallel with periodic centralized aggregations. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. Conventional approaches discard incomplete intra-model updates done by stragglers, alter the amount of local workload and architecture, or resort to asynchronous settings; which all affect the trained model performance under"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.18375","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/2403.18375/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":"2026-07-05T08:01:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hgXXGqLutfk4Zi+/Um0NSjoxpAaEN5x61uEVIlDCpnSWPQh0PJ7iHlDomp3thWwB4SyvwgQLNpOq+M3LRJ6PAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:19:15.091549Z"},"content_sha256":"4d675a4e60a2141c428c969ea23d7f24298935a66b80f9dd051faf4f3c94f1d0","schema_version":"1.0","event_id":"sha256:4d675a4e60a2141c428c969ea23d7f24298935a66b80f9dd051faf4f3c94f1d0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IVIGANRNT2SRF4EFIULQHFF2E5/bundle.json","state_url":"https://pith.science/pith/IVIGANRNT2SRF4EFIULQHFF2E5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IVIGANRNT2SRF4EFIULQHFF2E5/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":"2026-08-07T06:19:15Z","links":{"resolver":"https://pith.science/pith/IVIGANRNT2SRF4EFIULQHFF2E5","bundle":"https://pith.science/pith/IVIGANRNT2SRF4EFIULQHFF2E5/bundle.json","state":"https://pith.science/pith/IVIGANRNT2SRF4EFIULQHFF2E5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IVIGANRNT2SRF4EFIULQHFF2E5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:IVIGANRNT2SRF4EFIULQHFF2E5","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":"5dbfc10fb215872a896950e499473ca39906e3beefca54bd0d6645adfd9182de","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-03-27T09:14:36Z","title_canon_sha256":"dfe222dfc9239eee038e6d7ecf51d8f19f09f9028f2311390dbc2974d9843d41"},"schema_version":"1.0","source":{"id":"2403.18375","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.18375","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"arxiv_version","alias_value":"2403.18375v1","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.18375","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_12","alias_value":"IVIGANRNT2SR","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_16","alias_value":"IVIGANRNT2SRF4EF","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_8","alias_value":"IVIGANRN","created_at":"2026-07-05T08:01:21Z"}],"graph_snapshots":[{"event_id":"sha256:4d675a4e60a2141c428c969ea23d7f24298935a66b80f9dd051faf4f3c94f1d0","target":"graph","created_at":"2026-07-05T08:01:21Z","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/2403.18375/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN) models in parallel with periodic centralized aggregations. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. Conventional approaches discard incomplete intra-model updates done by stragglers, alter the amount of local workload and architecture, or resort to asynchronous settings; which all affect the trained model performance under","authors_text":"Alejandro Cohen, Natalie Lang, Nir Shlezinger","cross_cats":["eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-03-27T09:14:36Z","title":"Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.18375","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:f2d507df8ee458685d973f20f064d9dd53a050b05ea4fd337fb6cc281ade2214","target":"record","created_at":"2026-07-05T08:01:21Z","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":"5dbfc10fb215872a896950e499473ca39906e3beefca54bd0d6645adfd9182de","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-03-27T09:14:36Z","title_canon_sha256":"dfe222dfc9239eee038e6d7ecf51d8f19f09f9028f2311390dbc2974d9843d41"},"schema_version":"1.0","source":{"id":"2403.18375","kind":"arxiv","version":1}},"canonical_sha256":"455060362d9ea512f08545170394ba2756f50e76502ec278b8cb51f41f581cd3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"455060362d9ea512f08545170394ba2756f50e76502ec278b8cb51f41f581cd3","first_computed_at":"2026-07-05T08:01:21.859012Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:01:21.859012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tj4LHmjsrsgfyQe7nHxzwqv+3cfmbutLJnoW6qUoEMwgXwK9rkQjZCVCemGJN7pqKDh/wrhYgOXxLKr6aC1GCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:01:21.859431Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.18375","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f2d507df8ee458685d973f20f064d9dd53a050b05ea4fd337fb6cc281ade2214","sha256:4d675a4e60a2141c428c969ea23d7f24298935a66b80f9dd051faf4f3c94f1d0"],"state_sha256":"214bb29ae4395bea950b12c084c594bdc01fdf82aab88d9f0af10be160792b7f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qCxWqXAdl/xa8NUs5ra9XwFXftqjpDaLzsdGP1jpzsgmjDreSBZzSoHlZIXafL2VgyuKd+SvQH49RtopY2XuBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T06:19:15.095170Z","bundle_sha256":"e2efa8b97e660aff68acff7f3d88beece692187d49f7b582d23b2d3a7f1d75a7"}}