{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XEVN3ZDB2UX2SDG2T5DD3TGMB5","short_pith_number":"pith:XEVN3ZDB","schema_version":"1.0","canonical_sha256":"b92adde461d52fa90cda9f463dcccc0f4ff0b604453ab676f309fd5ef91b05bb","source":{"kind":"arxiv","id":"2405.00556","version":2},"attestation_state":"computed","paper":{"title":"Swarm Learning: A Survey of Concepts, Applications, and Trends","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Elham Shammar, Mohammed A. A. Al-qaness, Xiaohui Cui","submitted_at":"2024-05-01T14:59:24Z","abstract_excerpt":"Deep learning models have raised privacy and security concerns due to their reliance on large datasets on central servers. As the number of Internet of Things (IoT) devices increases, artificial intelligence (AI) will be crucial for resource management, data processing, and knowledge acquisition. To address those issues, federated learning (FL) has introduced a novel approach to building a versatile, large-scale machine learning framework that operates in a decentralized and hardware-agnostic manner. However, FL faces network bandwidth limitations and data breaches. To reduce the central depen"},"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":"2405.00556","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-01T14:59:24Z","cross_cats_sorted":[],"title_canon_sha256":"43c20270052a4145ea4a10b098765b26e55d43619d708ee90dbfecdab76cbef9","abstract_canon_sha256":"a7bca53d8567df41eaba6062f8796b4dcf7cedcc35ece6d4542339a555886282"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:21:03.034711Z","signature_b64":"Lwz7T+xoue8HeYu4DN8xZnUjqH2QlAAE/I7ZzVLM9mIIZmbcQORTnU6ewg56g04GiehlKTcv2CAfvHl90Z1QAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b92adde461d52fa90cda9f463dcccc0f4ff0b604453ab676f309fd5ef91b05bb","last_reissued_at":"2026-07-05T10:21:03.034266Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:21:03.034266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Swarm Learning: A Survey of Concepts, Applications, and Trends","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Elham Shammar, Mohammed A. A. Al-qaness, Xiaohui Cui","submitted_at":"2024-05-01T14:59:24Z","abstract_excerpt":"Deep learning models have raised privacy and security concerns due to their reliance on large datasets on central servers. As the number of Internet of Things (IoT) devices increases, artificial intelligence (AI) will be crucial for resource management, data processing, and knowledge acquisition. To address those issues, federated learning (FL) has introduced a novel approach to building a versatile, large-scale machine learning framework that operates in a decentralized and hardware-agnostic manner. However, FL faces network bandwidth limitations and data breaches. To reduce the central depen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.00556","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/2405.00556/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":"2405.00556","created_at":"2026-07-05T10:21:03.034335+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.00556v2","created_at":"2026-07-05T10:21:03.034335+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.00556","created_at":"2026-07-05T10:21:03.034335+00:00"},{"alias_kind":"pith_short_12","alias_value":"XEVN3ZDB2UX2","created_at":"2026-07-05T10:21:03.034335+00:00"},{"alias_kind":"pith_short_16","alias_value":"XEVN3ZDB2UX2SDG2","created_at":"2026-07-05T10:21:03.034335+00:00"},{"alias_kind":"pith_short_8","alias_value":"XEVN3ZDB","created_at":"2026-07-05T10:21:03.034335+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11803","citing_title":"SwarmSense-DNN: A Trustworthy and Decentralized Neural Framework for Proactive Anomaly Defense in Consumer IoT","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XEVN3ZDB2UX2SDG2T5DD3TGMB5","json":"https://pith.science/pith/XEVN3ZDB2UX2SDG2T5DD3TGMB5.json","graph_json":"https://pith.science/api/pith-number/XEVN3ZDB2UX2SDG2T5DD3TGMB5/graph.json","events_json":"https://pith.science/api/pith-number/XEVN3ZDB2UX2SDG2T5DD3TGMB5/events.json","paper":"https://pith.science/paper/XEVN3ZDB"},"agent_actions":{"view_html":"https://pith.science/pith/XEVN3ZDB2UX2SDG2T5DD3TGMB5","download_json":"https://pith.science/pith/XEVN3ZDB2UX2SDG2T5DD3TGMB5.json","view_paper":"https://pith.science/paper/XEVN3ZDB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.00556&json=true","fetch_graph":"https://pith.science/api/pith-number/XEVN3ZDB2UX2SDG2T5DD3TGMB5/graph.json","fetch_events":"https://pith.science/api/pith-number/XEVN3ZDB2UX2SDG2T5DD3TGMB5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XEVN3ZDB2UX2SDG2T5DD3TGMB5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XEVN3ZDB2UX2SDG2T5DD3TGMB5/action/storage_attestation","attest_author":"https://pith.science/pith/XEVN3ZDB2UX2SDG2T5DD3TGMB5/action/author_attestation","sign_citation":"https://pith.science/pith/XEVN3ZDB2UX2SDG2T5DD3TGMB5/action/citation_signature","submit_replication":"https://pith.science/pith/XEVN3ZDB2UX2SDG2T5DD3TGMB5/action/replication_record"}},"created_at":"2026-07-05T10:21:03.034335+00:00","updated_at":"2026-07-05T10:21:03.034335+00:00"}