{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:NEL4A3HJRBC3XUWFI2JBTNTN2E","short_pith_number":"pith:NEL4A3HJ","canonical_record":{"source":{"id":"2002.09574","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-21T23:06:20Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b658c7af6698fdc19f090b2046b77de44554292ef109160c9809b5872f5c369f","abstract_canon_sha256":"6812099523f9e342d5683a2e46adbe45307e4ad04477c014482aeccb7b204f4c"},"schema_version":"1.0"},"canonical_sha256":"6917c06ce98845bbd2c5469219b66dd13ecfd93b855bac9bd3f9f3c0cc386b92","source":{"kind":"arxiv","id":"2002.09574","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.09574","created_at":"2026-07-05T02:01:10Z"},{"alias_kind":"arxiv_version","alias_value":"2002.09574v2","created_at":"2026-07-05T02:01:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.09574","created_at":"2026-07-05T02:01:10Z"},{"alias_kind":"pith_short_12","alias_value":"NEL4A3HJRBC3","created_at":"2026-07-05T02:01:10Z"},{"alias_kind":"pith_short_16","alias_value":"NEL4A3HJRBC3XUWF","created_at":"2026-07-05T02:01:10Z"},{"alias_kind":"pith_short_8","alias_value":"NEL4A3HJ","created_at":"2026-07-05T02:01:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:NEL4A3HJRBC3XUWFI2JBTNTN2E","target":"record","payload":{"canonical_record":{"source":{"id":"2002.09574","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-21T23:06:20Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b658c7af6698fdc19f090b2046b77de44554292ef109160c9809b5872f5c369f","abstract_canon_sha256":"6812099523f9e342d5683a2e46adbe45307e4ad04477c014482aeccb7b204f4c"},"schema_version":"1.0"},"canonical_sha256":"6917c06ce98845bbd2c5469219b66dd13ecfd93b855bac9bd3f9f3c0cc386b92","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:01:10.914967Z","signature_b64":"AaE8FgXkcSZEsUct8ibPF6A8sxRRksWjth0cT+s88qwBrULPGMFyqt/+f5RxTeQ0d1UK4ghP1nzsvpJzpQucBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6917c06ce98845bbd2c5469219b66dd13ecfd93b855bac9bd3f9f3c0cc386b92","last_reissued_at":"2026-07-05T02:01:10.914540Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:01:10.914540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.09574","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":"2026-07-05T02:01:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b/dJxNlLXcxjjkomTfO3+SBIT0yAwpaw0X7Ov/YbleztJDXGorEj4BcKwZPVNhkjmcrSs3DBRARswqUGWXPfCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T19:54:03.126525Z"},"content_sha256":"8a66d5529083b392b60c0e1186888d946669cfb1fe3e84bd4e63e04997af8177","schema_version":"1.0","event_id":"sha256:8a66d5529083b392b60c0e1186888d946669cfb1fe3e84bd4e63e04997af8177"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:NEL4A3HJRBC3XUWFI2JBTNTN2E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Coded Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Nageen Himayat, Sagar Dhakal, Saurav Prakash, Shilpa Talwar, Yair Yona","submitted_at":"2020-02-21T23:06:20Z","abstract_excerpt":"Federated learning is a method of training a global model from decentralized data distributed across client devices. Here, model parameters are computed locally by each client device and exchanged with a central server, which aggregates the local models for a global view, without requiring sharing of training data. The convergence performance of federated learning is severely impacted in heterogeneous computing platforms such as those at the wireless edge, where straggling computations and communication links can significantly limit timely model parameter updates. This paper develops a novel c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.09574","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/2002.09574/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":"2026-07-05T02:01:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uxliRH/MVccyEuyZ+rzj0jSlIe/mmLDoPjoiACFJ7Kob46YwllcdAsbJVSi2UKgXnXyse1yS4ZKqaQjZD2KpDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T19:54:03.126870Z"},"content_sha256":"0dd3f3bfa0678f931216e5cca5e3eab0aa37c59d413d660183af16e4f4f6e306","schema_version":"1.0","event_id":"sha256:0dd3f3bfa0678f931216e5cca5e3eab0aa37c59d413d660183af16e4f4f6e306"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NEL4A3HJRBC3XUWFI2JBTNTN2E/bundle.json","state_url":"https://pith.science/pith/NEL4A3HJRBC3XUWFI2JBTNTN2E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NEL4A3HJRBC3XUWFI2JBTNTN2E/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":"2026-08-23T19:54:03Z","links":{"resolver":"https://pith.science/pith/NEL4A3HJRBC3XUWFI2JBTNTN2E","bundle":"https://pith.science/pith/NEL4A3HJRBC3XUWFI2JBTNTN2E/bundle.json","state":"https://pith.science/pith/NEL4A3HJRBC3XUWFI2JBTNTN2E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NEL4A3HJRBC3XUWFI2JBTNTN2E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:NEL4A3HJRBC3XUWFI2JBTNTN2E","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":"6812099523f9e342d5683a2e46adbe45307e4ad04477c014482aeccb7b204f4c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-21T23:06:20Z","title_canon_sha256":"b658c7af6698fdc19f090b2046b77de44554292ef109160c9809b5872f5c369f"},"schema_version":"1.0","source":{"id":"2002.09574","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.09574","created_at":"2026-07-05T02:01:10Z"},{"alias_kind":"arxiv_version","alias_value":"2002.09574v2","created_at":"2026-07-05T02:01:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.09574","created_at":"2026-07-05T02:01:10Z"},{"alias_kind":"pith_short_12","alias_value":"NEL4A3HJRBC3","created_at":"2026-07-05T02:01:10Z"},{"alias_kind":"pith_short_16","alias_value":"NEL4A3HJRBC3XUWF","created_at":"2026-07-05T02:01:10Z"},{"alias_kind":"pith_short_8","alias_value":"NEL4A3HJ","created_at":"2026-07-05T02:01:10Z"}],"graph_snapshots":[{"event_id":"sha256:0dd3f3bfa0678f931216e5cca5e3eab0aa37c59d413d660183af16e4f4f6e306","target":"graph","created_at":"2026-07-05T02:01:10Z","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/2002.09574/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Federated learning is a method of training a global model from decentralized data distributed across client devices. Here, model parameters are computed locally by each client device and exchanged with a central server, which aggregates the local models for a global view, without requiring sharing of training data. The convergence performance of federated learning is severely impacted in heterogeneous computing platforms such as those at the wireless edge, where straggling computations and communication links can significantly limit timely model parameter updates. This paper develops a novel c","authors_text":"Nageen Himayat, Sagar Dhakal, Saurav Prakash, Shilpa Talwar, Yair Yona","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-21T23:06:20Z","title":"Coded Federated Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.09574","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":"sha256:8a66d5529083b392b60c0e1186888d946669cfb1fe3e84bd4e63e04997af8177","target":"record","created_at":"2026-07-05T02:01:10Z","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":"6812099523f9e342d5683a2e46adbe45307e4ad04477c014482aeccb7b204f4c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-21T23:06:20Z","title_canon_sha256":"b658c7af6698fdc19f090b2046b77de44554292ef109160c9809b5872f5c369f"},"schema_version":"1.0","source":{"id":"2002.09574","kind":"arxiv","version":2}},"canonical_sha256":"6917c06ce98845bbd2c5469219b66dd13ecfd93b855bac9bd3f9f3c0cc386b92","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6917c06ce98845bbd2c5469219b66dd13ecfd93b855bac9bd3f9f3c0cc386b92","first_computed_at":"2026-07-05T02:01:10.914540Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:01:10.914540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AaE8FgXkcSZEsUct8ibPF6A8sxRRksWjth0cT+s88qwBrULPGMFyqt/+f5RxTeQ0d1UK4ghP1nzsvpJzpQucBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:01:10.914967Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.09574","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8a66d5529083b392b60c0e1186888d946669cfb1fe3e84bd4e63e04997af8177","sha256:0dd3f3bfa0678f931216e5cca5e3eab0aa37c59d413d660183af16e4f4f6e306"],"state_sha256":"5dfd010c3b07942e53f7d520d9bda58386034bb9ed498b4e6dbb461ce8a117a6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PFm063l9zUn7+lZYcVgvd272ogE0WmAxK/vRu+yERufxvVDtRdWYKl2/exU0JbFsinT3ec9K2OQ/JbgJBoQmBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T19:54:03.129825Z","bundle_sha256":"446dd86e813163b3620b1659be37c53cc3511221fadabbaee08dc272f18d7245"}}