{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZPW62TZ2J4L7E7EKBPKBAUFJNR","short_pith_number":"pith:ZPW62TZ2","schema_version":"1.0","canonical_sha256":"cbeded4f3a4f17f27c8a0bd41050a96c5d37ef8061e1f16124b5b80d83c8523b","source":{"kind":"arxiv","id":"2605.27601","version":1},"attestation_state":"computed","paper":{"title":"A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.PF"],"primary_cat":"cs.DC","authors_text":"Chaimae Jallouli, Karim Boubouh, Robert Basmadjian","submitted_at":"2026-05-26T19:19:31Z","abstract_excerpt":"Estimating CPU power on heterogeneous ARM-based commodity devices is challenging due to limited access to CPU's voltage domains. As a result, state-of-the-art energy-aware Federated Learning (FL) frameworks typically rely on simplified approximate power models to estimate computation energy, rather than the more accurate analytical CMOS-based model. To bridge this gap, we propose a reproducible CPU power estimation methodology combined with a rail-to-cluster mapping technique to retrieve cluster-level supply voltage. We evaluate our approach on two commodity Android devices and show that the a"},"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":"2605.27601","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2026-05-26T19:19:31Z","cross_cats_sorted":["cs.LG","cs.PF"],"title_canon_sha256":"0bc0dc185d9fcd8ebdea40ecbac02541018ff72498c16a56041c9d7c2a6349b9","abstract_canon_sha256":"dafbd07882f6f914f788e6ada8fa793702ae81bbc2d2fbfb23f1d7e8d273710a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:17.369793Z","signature_b64":"p3ESRp1Q4YO8hsxeL1vMNhPed24DIsX93w4JgY8bocRlqQ4e0QyuQ6uN/FpdRuU/8S2TV8q8s/J/QQ4Poz3vCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cbeded4f3a4f17f27c8a0bd41050a96c5d37ef8061e1f16124b5b80d83c8523b","last_reissued_at":"2026-05-28T01:04:17.369341Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:17.369341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.PF"],"primary_cat":"cs.DC","authors_text":"Chaimae Jallouli, Karim Boubouh, Robert Basmadjian","submitted_at":"2026-05-26T19:19:31Z","abstract_excerpt":"Estimating CPU power on heterogeneous ARM-based commodity devices is challenging due to limited access to CPU's voltage domains. As a result, state-of-the-art energy-aware Federated Learning (FL) frameworks typically rely on simplified approximate power models to estimate computation energy, rather than the more accurate analytical CMOS-based model. To bridge this gap, we propose a reproducible CPU power estimation methodology combined with a rail-to-cluster mapping technique to retrieve cluster-level supply voltage. We evaluate our approach on two commodity Android devices and show that the a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.27601","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/2605.27601/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":"2605.27601","created_at":"2026-05-28T01:04:17.369400+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.27601v1","created_at":"2026-05-28T01:04:17.369400+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.27601","created_at":"2026-05-28T01:04:17.369400+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZPW62TZ2J4L7","created_at":"2026-05-28T01:04:17.369400+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZPW62TZ2J4L7E7EK","created_at":"2026-05-28T01:04:17.369400+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZPW62TZ2","created_at":"2026-05-28T01:04:17.369400+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/ZPW62TZ2J4L7E7EKBPKBAUFJNR","json":"https://pith.science/pith/ZPW62TZ2J4L7E7EKBPKBAUFJNR.json","graph_json":"https://pith.science/api/pith-number/ZPW62TZ2J4L7E7EKBPKBAUFJNR/graph.json","events_json":"https://pith.science/api/pith-number/ZPW62TZ2J4L7E7EKBPKBAUFJNR/events.json","paper":"https://pith.science/paper/ZPW62TZ2"},"agent_actions":{"view_html":"https://pith.science/pith/ZPW62TZ2J4L7E7EKBPKBAUFJNR","download_json":"https://pith.science/pith/ZPW62TZ2J4L7E7EKBPKBAUFJNR.json","view_paper":"https://pith.science/paper/ZPW62TZ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.27601&json=true","fetch_graph":"https://pith.science/api/pith-number/ZPW62TZ2J4L7E7EKBPKBAUFJNR/graph.json","fetch_events":"https://pith.science/api/pith-number/ZPW62TZ2J4L7E7EKBPKBAUFJNR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZPW62TZ2J4L7E7EKBPKBAUFJNR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZPW62TZ2J4L7E7EKBPKBAUFJNR/action/storage_attestation","attest_author":"https://pith.science/pith/ZPW62TZ2J4L7E7EKBPKBAUFJNR/action/author_attestation","sign_citation":"https://pith.science/pith/ZPW62TZ2J4L7E7EKBPKBAUFJNR/action/citation_signature","submit_replication":"https://pith.science/pith/ZPW62TZ2J4L7E7EKBPKBAUFJNR/action/replication_record"}},"created_at":"2026-05-28T01:04:17.369400+00:00","updated_at":"2026-05-28T01:04:17.369400+00:00"}