{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UE56GXCSMUBLH4SBY7YN7MSYBB","short_pith_number":"pith:UE56GXCS","schema_version":"1.0","canonical_sha256":"a13be35c526502b3f241c7f0dfb258084d612d66b159c79dad3977df420d543f","source":{"kind":"arxiv","id":"2405.00394","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.GT","authors_text":"Azzam Mourad, Bassem Ouni, Hadi Otrok, Hoda Al khzaimi, Mohsen Guizani, Omar Abdel Wahab, Osama Wehbi, Sarhad Arisdakessian","submitted_at":"2024-05-01T08:49:22Z","abstract_excerpt":"Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system's integrity due to the possibility of malicious client selection. Primarily, the existing literature focuses on assessing the trustworthiness of clients, neglecting the crucial aspect of "},"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":"2405.00394","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.GT","submitted_at":"2024-05-01T08:49:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e5afdad304d3d943ec6467afa2884ef1c9430663afff047fb02d661a81b40738","abstract_canon_sha256":"7a5b9184d0f0b5072e0c50837cbdfcf4fef2d1fe4ca70a405c8e052068d1af2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:12.428231Z","signature_b64":"VxscBuH+1r5rdsPXu5vMHlVz8aHlEYNMOo+U+AnmeapjAH29xvIzqdINM5ZWO70PGORLhIxJ6sXIF6KQKNVDBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a13be35c526502b3f241c7f0dfb258084d612d66b159c79dad3977df420d543f","last_reissued_at":"2026-07-05T08:14:12.427798Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:12.427798Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.GT","authors_text":"Azzam Mourad, Bassem Ouni, Hadi Otrok, Hoda Al khzaimi, Mohsen Guizani, Omar Abdel Wahab, Osama Wehbi, Sarhad Arisdakessian","submitted_at":"2024-05-01T08:49:22Z","abstract_excerpt":"Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system's integrity due to the possibility of malicious client selection. Primarily, the existing literature focuses on assessing the trustworthiness of clients, neglecting the crucial aspect of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.00394","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/2405.00394/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":"2405.00394","created_at":"2026-07-05T08:14:12.427849+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.00394v1","created_at":"2026-07-05T08:14:12.427849+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.00394","created_at":"2026-07-05T08:14:12.427849+00:00"},{"alias_kind":"pith_short_12","alias_value":"UE56GXCSMUBL","created_at":"2026-07-05T08:14:12.427849+00:00"},{"alias_kind":"pith_short_16","alias_value":"UE56GXCSMUBLH4SB","created_at":"2026-07-05T08:14:12.427849+00:00"},{"alias_kind":"pith_short_8","alias_value":"UE56GXCS","created_at":"2026-07-05T08:14:12.427849+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UE56GXCSMUBLH4SBY7YN7MSYBB","json":"https://pith.science/pith/UE56GXCSMUBLH4SBY7YN7MSYBB.json","graph_json":"https://pith.science/api/pith-number/UE56GXCSMUBLH4SBY7YN7MSYBB/graph.json","events_json":"https://pith.science/api/pith-number/UE56GXCSMUBLH4SBY7YN7MSYBB/events.json","paper":"https://pith.science/paper/UE56GXCS"},"agent_actions":{"view_html":"https://pith.science/pith/UE56GXCSMUBLH4SBY7YN7MSYBB","download_json":"https://pith.science/pith/UE56GXCSMUBLH4SBY7YN7MSYBB.json","view_paper":"https://pith.science/paper/UE56GXCS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.00394&json=true","fetch_graph":"https://pith.science/api/pith-number/UE56GXCSMUBLH4SBY7YN7MSYBB/graph.json","fetch_events":"https://pith.science/api/pith-number/UE56GXCSMUBLH4SBY7YN7MSYBB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UE56GXCSMUBLH4SBY7YN7MSYBB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UE56GXCSMUBLH4SBY7YN7MSYBB/action/storage_attestation","attest_author":"https://pith.science/pith/UE56GXCSMUBLH4SBY7YN7MSYBB/action/author_attestation","sign_citation":"https://pith.science/pith/UE56GXCSMUBLH4SBY7YN7MSYBB/action/citation_signature","submit_replication":"https://pith.science/pith/UE56GXCSMUBLH4SBY7YN7MSYBB/action/replication_record"}},"created_at":"2026-07-05T08:14:12.427849+00:00","updated_at":"2026-07-05T08:14:12.427849+00:00"}