{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:Z7SYGUBR5HBFE7ONJHBIRLYX5C","short_pith_number":"pith:Z7SYGUBR","schema_version":"1.0","canonical_sha256":"cfe5835031e9c2527dcd49c288af17e8bbb3cb2627cf823ba50cabe71f947f3a","source":{"kind":"arxiv","id":"2112.05687","version":1},"attestation_state":"computed","paper":{"title":"Federated Two-stage Learning with Sign-based Voting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Jinfeng Yi, Shuguang Cui, Wenye Li, Yu Lu, Zichen Ma, Zihan Lu","submitted_at":"2021-12-10T17:31:23Z","abstract_excerpt":"Federated learning is a distributed machine learning mechanism where local devices collaboratively train a shared global model under the orchestration of a central server, while keeping all private data decentralized. In the system, model parameters and its updates are transmitted instead of raw data, and thus the communication bottleneck has become a key challenge. Besides, recent larger and deeper machine learning models also pose more difficulties in deploying them in a federated environment. In this paper, we design a federated two-stage learning framework that augments prototypical federa"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2112.05687","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2021-12-10T17:31:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b350040ee2f9359bd0aff478a0245c22c3e219dcaf2e6f89d9cf7777f47fa16c","abstract_canon_sha256":"a55ffa6e8e8e5649e295c28ace381cb13434605236e5ecab9ada25713e8e16eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:39:50.815091Z","signature_b64":"gyUI9Z6BbkjhbhC9r159nV3rlbDZPmHM6f9im6ugLqPA2lYenz7ta8rHxjQoGlEryfSTQeTxr4+tnAssvnqmBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cfe5835031e9c2527dcd49c288af17e8bbb3cb2627cf823ba50cabe71f947f3a","last_reissued_at":"2026-07-05T03:39:50.814703Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:39:50.814703Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Two-stage Learning with Sign-based Voting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Jinfeng Yi, Shuguang Cui, Wenye Li, Yu Lu, Zichen Ma, Zihan Lu","submitted_at":"2021-12-10T17:31:23Z","abstract_excerpt":"Federated learning is a distributed machine learning mechanism where local devices collaboratively train a shared global model under the orchestration of a central server, while keeping all private data decentralized. In the system, model parameters and its updates are transmitted instead of raw data, and thus the communication bottleneck has become a key challenge. Besides, recent larger and deeper machine learning models also pose more difficulties in deploying them in a federated environment. In this paper, we design a federated two-stage learning framework that augments prototypical federa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.05687","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/2112.05687/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2112.05687","created_at":"2026-07-05T03:39:50.814761+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.05687v1","created_at":"2026-07-05T03:39:50.814761+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.05687","created_at":"2026-07-05T03:39:50.814761+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z7SYGUBR5HBF","created_at":"2026-07-05T03:39:50.814761+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z7SYGUBR5HBFE7ON","created_at":"2026-07-05T03:39:50.814761+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z7SYGUBR","created_at":"2026-07-05T03:39:50.814761+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03973","citing_title":"One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning","ref_index":53,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z7SYGUBR5HBFE7ONJHBIRLYX5C","json":"https://pith.science/pith/Z7SYGUBR5HBFE7ONJHBIRLYX5C.json","graph_json":"https://pith.science/api/pith-number/Z7SYGUBR5HBFE7ONJHBIRLYX5C/graph.json","events_json":"https://pith.science/api/pith-number/Z7SYGUBR5HBFE7ONJHBIRLYX5C/events.json","paper":"https://pith.science/paper/Z7SYGUBR"},"agent_actions":{"view_html":"https://pith.science/pith/Z7SYGUBR5HBFE7ONJHBIRLYX5C","download_json":"https://pith.science/pith/Z7SYGUBR5HBFE7ONJHBIRLYX5C.json","view_paper":"https://pith.science/paper/Z7SYGUBR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.05687&json=true","fetch_graph":"https://pith.science/api/pith-number/Z7SYGUBR5HBFE7ONJHBIRLYX5C/graph.json","fetch_events":"https://pith.science/api/pith-number/Z7SYGUBR5HBFE7ONJHBIRLYX5C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z7SYGUBR5HBFE7ONJHBIRLYX5C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z7SYGUBR5HBFE7ONJHBIRLYX5C/action/storage_attestation","attest_author":"https://pith.science/pith/Z7SYGUBR5HBFE7ONJHBIRLYX5C/action/author_attestation","sign_citation":"https://pith.science/pith/Z7SYGUBR5HBFE7ONJHBIRLYX5C/action/citation_signature","submit_replication":"https://pith.science/pith/Z7SYGUBR5HBFE7ONJHBIRLYX5C/action/replication_record"}},"created_at":"2026-07-05T03:39:50.814761+00:00","updated_at":"2026-07-05T03:39:50.814761+00:00"}