{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DNQZJNHFRXU52ZHFV4TNTESQHR","short_pith_number":"pith:DNQZJNHF","schema_version":"1.0","canonical_sha256":"1b6194b4e58de9dd64e5af26d992503c4c3f0c71040c60744bacff91161fa948","source":{"kind":"arxiv","id":"2511.11949","version":2},"attestation_state":"computed","paper":{"title":"Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Eunjeong Jeong, Nikolaos Pappas","submitted_at":"2025-11-14T23:46:48Z","abstract_excerpt":"Federated learning (FL) is a powerful paradigm for distributed learning, but increasing model complexity leads to significant energy consumption from client-side computations for local training. This challenge is critical in energy-harvesting FL (EHFL) systems, where the participation availability of each device fluctuates because of limited energy. To address this, we propose PipeCycle, a battery-aware distributed learning framework that organizes clients into pipelined cyclic groups. When a group completes its intra-group aggregation, its aggregated model is relayed directly to a newly forme"},"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":"2511.11949","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-11-14T23:46:48Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"0f6975ded0aaf91bbc7dd9692906e61423522f4081d2a6286cf4507eaac3fb31","abstract_canon_sha256":"9919da6e159794860adc03063b715fbf1ccfd068d505559177156ac72e9ef6c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-16T01:22:30.175870Z","signature_b64":"KyBKGwIh+cyDe3HASRr0zoNygg3t/Y6jpigiKbfDawI5M+fjoW4qm0rFYnnDMxgrZc5ZHJzH0eKscJ91YmLyCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b6194b4e58de9dd64e5af26d992503c4c3f0c71040c60744bacff91161fa948","last_reissued_at":"2026-07-16T01:22:30.174958Z","signature_status":"signed_v1","first_computed_at":"2026-07-16T01:22:30.174958Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Eunjeong Jeong, Nikolaos Pappas","submitted_at":"2025-11-14T23:46:48Z","abstract_excerpt":"Federated learning (FL) is a powerful paradigm for distributed learning, but increasing model complexity leads to significant energy consumption from client-side computations for local training. This challenge is critical in energy-harvesting FL (EHFL) systems, where the participation availability of each device fluctuates because of limited energy. To address this, we propose PipeCycle, a battery-aware distributed learning framework that organizes clients into pipelined cyclic groups. When a group completes its intra-group aggregation, its aggregated model is relayed directly to a newly forme"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.11949","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/2511.11949/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":"2511.11949","created_at":"2026-07-16T01:22:30.175395+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.11949v2","created_at":"2026-07-16T01:22:30.175395+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.11949","created_at":"2026-07-16T01:22:30.175395+00:00"},{"alias_kind":"pith_short_12","alias_value":"DNQZJNHFRXU5","created_at":"2026-07-16T01:22:30.175395+00:00"},{"alias_kind":"pith_short_16","alias_value":"DNQZJNHFRXU52ZHF","created_at":"2026-07-16T01:22:30.175395+00:00"},{"alias_kind":"pith_short_8","alias_value":"DNQZJNHF","created_at":"2026-07-16T01:22:30.175395+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/DNQZJNHFRXU52ZHFV4TNTESQHR","json":"https://pith.science/pith/DNQZJNHFRXU52ZHFV4TNTESQHR.json","graph_json":"https://pith.science/api/pith-number/DNQZJNHFRXU52ZHFV4TNTESQHR/graph.json","events_json":"https://pith.science/api/pith-number/DNQZJNHFRXU52ZHFV4TNTESQHR/events.json","paper":"https://pith.science/paper/DNQZJNHF"},"agent_actions":{"view_html":"https://pith.science/pith/DNQZJNHFRXU52ZHFV4TNTESQHR","download_json":"https://pith.science/pith/DNQZJNHFRXU52ZHFV4TNTESQHR.json","view_paper":"https://pith.science/paper/DNQZJNHF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.11949&json=true","fetch_graph":"https://pith.science/api/pith-number/DNQZJNHFRXU52ZHFV4TNTESQHR/graph.json","fetch_events":"https://pith.science/api/pith-number/DNQZJNHFRXU52ZHFV4TNTESQHR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DNQZJNHFRXU52ZHFV4TNTESQHR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DNQZJNHFRXU52ZHFV4TNTESQHR/action/storage_attestation","attest_author":"https://pith.science/pith/DNQZJNHFRXU52ZHFV4TNTESQHR/action/author_attestation","sign_citation":"https://pith.science/pith/DNQZJNHFRXU52ZHFV4TNTESQHR/action/citation_signature","submit_replication":"https://pith.science/pith/DNQZJNHFRXU52ZHFV4TNTESQHR/action/replication_record"}},"created_at":"2026-07-16T01:22:30.175395+00:00","updated_at":"2026-07-16T01:22:30.175395+00:00"}