{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SIDJBIVLBJQZJY6PW2RZN2EI53","short_pith_number":"pith:SIDJBIVL","schema_version":"1.0","canonical_sha256":"920690a2ab0a6194e3cfb6a396e888eeebd0c0f2ddbed9bc2d327f489c48834b","source":{"kind":"arxiv","id":"2412.19979","version":1},"attestation_state":"computed","paper":{"title":"Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Feibo Jiang, Kezhi Wang, Kun Yang, Li Dong, Yubo Peng","submitted_at":"2024-12-28T02:45:15Z","abstract_excerpt":"In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. The"},"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":"2412.19979","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-28T02:45:15Z","cross_cats_sorted":["cs.CR","cs.IT","math.IT"],"title_canon_sha256":"20da950615653d4cf7f7981f82308a86152c457e4d696e7b4831dbd1f4acd5ea","abstract_canon_sha256":"54fd0fdde6e5170f306150835903a3d10ce18dd8e84d1c830f0c9e1d74c6c529"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:52.577217Z","signature_b64":"TbKcK31P2X+n9gxc235t9HAy9D9asNd8g6kvgjc0Ii27X2QBOdk1UtzQ4mtZZycC8SqLB79rkGzbpcSuQP4bDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"920690a2ab0a6194e3cfb6a396e888eeebd0c0f2ddbed9bc2d327f489c48834b","last_reissued_at":"2026-07-05T09:54:52.576723Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:52.576723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Feibo Jiang, Kezhi Wang, Kun Yang, Li Dong, Yubo Peng","submitted_at":"2024-12-28T02:45:15Z","abstract_excerpt":"In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. The"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19979","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/2412.19979/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":"2412.19979","created_at":"2026-07-05T09:54:52.576780+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.19979v1","created_at":"2026-07-05T09:54:52.576780+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19979","created_at":"2026-07-05T09:54:52.576780+00:00"},{"alias_kind":"pith_short_12","alias_value":"SIDJBIVLBJQZ","created_at":"2026-07-05T09:54:52.576780+00:00"},{"alias_kind":"pith_short_16","alias_value":"SIDJBIVLBJQZJY6P","created_at":"2026-07-05T09:54:52.576780+00:00"},{"alias_kind":"pith_short_8","alias_value":"SIDJBIVL","created_at":"2026-07-05T09:54:52.576780+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/SIDJBIVLBJQZJY6PW2RZN2EI53","json":"https://pith.science/pith/SIDJBIVLBJQZJY6PW2RZN2EI53.json","graph_json":"https://pith.science/api/pith-number/SIDJBIVLBJQZJY6PW2RZN2EI53/graph.json","events_json":"https://pith.science/api/pith-number/SIDJBIVLBJQZJY6PW2RZN2EI53/events.json","paper":"https://pith.science/paper/SIDJBIVL"},"agent_actions":{"view_html":"https://pith.science/pith/SIDJBIVLBJQZJY6PW2RZN2EI53","download_json":"https://pith.science/pith/SIDJBIVLBJQZJY6PW2RZN2EI53.json","view_paper":"https://pith.science/paper/SIDJBIVL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.19979&json=true","fetch_graph":"https://pith.science/api/pith-number/SIDJBIVLBJQZJY6PW2RZN2EI53/graph.json","fetch_events":"https://pith.science/api/pith-number/SIDJBIVLBJQZJY6PW2RZN2EI53/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SIDJBIVLBJQZJY6PW2RZN2EI53/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SIDJBIVLBJQZJY6PW2RZN2EI53/action/storage_attestation","attest_author":"https://pith.science/pith/SIDJBIVLBJQZJY6PW2RZN2EI53/action/author_attestation","sign_citation":"https://pith.science/pith/SIDJBIVLBJQZJY6PW2RZN2EI53/action/citation_signature","submit_replication":"https://pith.science/pith/SIDJBIVLBJQZJY6PW2RZN2EI53/action/replication_record"}},"created_at":"2026-07-05T09:54:52.576780+00:00","updated_at":"2026-07-05T09:54:52.576780+00:00"}