{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:E6Z7EW6LHA6EOJ7S6PQUAEVWTQ","short_pith_number":"pith:E6Z7EW6L","schema_version":"1.0","canonical_sha256":"27b3f25bcb383c4727f2f3e14012b69c121e8c61eaf5572d3aa01e245ef4a7cd","source":{"kind":"arxiv","id":"2507.17772","version":1},"attestation_state":"computed","paper":{"title":"Caching Techniques for Reducing the Communication Cost of Federated Learning in IoT Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.DC","authors_text":"Ahmad Alhonainy (1), Praveen Rao (1) ((1) University of Missouri, USA)","submitted_at":"2025-07-19T17:02:15Z","abstract_excerpt":"Federated Learning (FL) allows multiple distributed devices to jointly train a shared model without centralizing data, but communication cost remains a major bottleneck, especially in resource-constrained environments. This paper introduces caching strategies - FIFO, LRU, and Priority-Based - to reduce unnecessary model update transmissions. By selectively forwarding significant updates, our approach lowers bandwidth usage while maintaining model accuracy. Experiments on CIFAR-10 and medical datasets show reduced communication with minimal accuracy loss. Results confirm that intelligent cachin"},"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":"2507.17772","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-07-19T17:02:15Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"20fcd5f693002e8f7b49ce9e6aeae21d0b2e58b8bf02616fe1d2adc837f17e0e","abstract_canon_sha256":"10eefaf89fe618ec15c380125f66dc95521a414fa29ce225d3f130f2385a903e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:34.837075Z","signature_b64":"kn+q4Mnlt4M5VZAbWfNwrO1yNlj7hHGtFst7qJ02LHHibIePHJ8SZCVIQEgaTrPekBvOWYdf+g3dCZlEoc8IBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27b3f25bcb383c4727f2f3e14012b69c121e8c61eaf5572d3aa01e245ef4a7cd","last_reissued_at":"2026-07-05T11:42:34.836524Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:34.836524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Caching Techniques for Reducing the Communication Cost of Federated Learning in IoT Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.DC","authors_text":"Ahmad Alhonainy (1), Praveen Rao (1) ((1) University of Missouri, USA)","submitted_at":"2025-07-19T17:02:15Z","abstract_excerpt":"Federated Learning (FL) allows multiple distributed devices to jointly train a shared model without centralizing data, but communication cost remains a major bottleneck, especially in resource-constrained environments. This paper introduces caching strategies - FIFO, LRU, and Priority-Based - to reduce unnecessary model update transmissions. By selectively forwarding significant updates, our approach lowers bandwidth usage while maintaining model accuracy. Experiments on CIFAR-10 and medical datasets show reduced communication with minimal accuracy loss. Results confirm that intelligent cachin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.17772","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/2507.17772/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":"2507.17772","created_at":"2026-07-05T11:42:34.836587+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.17772v1","created_at":"2026-07-05T11:42:34.836587+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.17772","created_at":"2026-07-05T11:42:34.836587+00:00"},{"alias_kind":"pith_short_12","alias_value":"E6Z7EW6LHA6E","created_at":"2026-07-05T11:42:34.836587+00:00"},{"alias_kind":"pith_short_16","alias_value":"E6Z7EW6LHA6EOJ7S","created_at":"2026-07-05T11:42:34.836587+00:00"},{"alias_kind":"pith_short_8","alias_value":"E6Z7EW6L","created_at":"2026-07-05T11:42:34.836587+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/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ","json":"https://pith.science/pith/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ.json","graph_json":"https://pith.science/api/pith-number/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ/graph.json","events_json":"https://pith.science/api/pith-number/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ/events.json","paper":"https://pith.science/paper/E6Z7EW6L"},"agent_actions":{"view_html":"https://pith.science/pith/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ","download_json":"https://pith.science/pith/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ.json","view_paper":"https://pith.science/paper/E6Z7EW6L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.17772&json=true","fetch_graph":"https://pith.science/api/pith-number/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ/graph.json","fetch_events":"https://pith.science/api/pith-number/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ/action/storage_attestation","attest_author":"https://pith.science/pith/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ/action/author_attestation","sign_citation":"https://pith.science/pith/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ/action/citation_signature","submit_replication":"https://pith.science/pith/E6Z7EW6LHA6EOJ7S6PQUAEVWTQ/action/replication_record"}},"created_at":"2026-07-05T11:42:34.836587+00:00","updated_at":"2026-07-05T11:42:34.836587+00:00"}