{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:V24XOQ4MCW35OE75FDUSTJ2XDR","short_pith_number":"pith:V24XOQ4M","schema_version":"1.0","canonical_sha256":"aeb977438c15b7d713fd28e929a7571c68473808f813c1272c0f22264fc5efd6","source":{"kind":"arxiv","id":"2502.05041","version":1},"attestation_state":"computed","paper":{"title":"Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC"],"primary_cat":"cs.LG","authors_text":"Apurva Narayan, Damitha Senevirathne, Dumindu Tissera, Katarina Grolinger, Miriam A.M. Capretz, Yohannis Kifle Telila","submitted_at":"2025-02-07T16:08:20Z","abstract_excerpt":"Anomaly detection is crucial in the energy sector to identify irregular patterns indicating equipment failures, energy theft, or other issues. Machine learning techniques for anomaly detection have achieved great success, but are typically centralized, involving sharing local data with a central server which raises privacy and security concerns. Federated Learning (FL) has been gaining popularity as it enables distributed learning without sharing local data. However, FL depends on neural networks, which are vulnerable to adversarial attacks that manipulate data, leading models to make erroneou"},"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":"2502.05041","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T16:08:20Z","cross_cats_sorted":["cs.AI","cs.DC"],"title_canon_sha256":"f985bdd4e7dbe7b6e3f72a2f61a55c147c9f32e59e6de6c36a96a5bf27cc81d0","abstract_canon_sha256":"f142a624e147d1187dedf70108daa29497f0c77a008a802157118604aaaa3386"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:02.221393Z","signature_b64":"T3VGUP+TZD6/Dx578iNtdUSiemyrVyrvDA5z+oMck9yi+bhOEX5O3F3rhl+ijrrxdlgH/Rt1Rr6PNg4IVC3eBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aeb977438c15b7d713fd28e929a7571c68473808f813c1272c0f22264fc5efd6","last_reissued_at":"2026-07-05T10:11:02.220848Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:02.220848Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DC"],"primary_cat":"cs.LG","authors_text":"Apurva Narayan, Damitha Senevirathne, Dumindu Tissera, Katarina Grolinger, Miriam A.M. Capretz, Yohannis Kifle Telila","submitted_at":"2025-02-07T16:08:20Z","abstract_excerpt":"Anomaly detection is crucial in the energy sector to identify irregular patterns indicating equipment failures, energy theft, or other issues. Machine learning techniques for anomaly detection have achieved great success, but are typically centralized, involving sharing local data with a central server which raises privacy and security concerns. Federated Learning (FL) has been gaining popularity as it enables distributed learning without sharing local data. However, FL depends on neural networks, which are vulnerable to adversarial attacks that manipulate data, leading models to make erroneou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05041","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/2502.05041/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":"2502.05041","created_at":"2026-07-05T10:11:02.220923+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.05041v1","created_at":"2026-07-05T10:11:02.220923+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05041","created_at":"2026-07-05T10:11:02.220923+00:00"},{"alias_kind":"pith_short_12","alias_value":"V24XOQ4MCW35","created_at":"2026-07-05T10:11:02.220923+00:00"},{"alias_kind":"pith_short_16","alias_value":"V24XOQ4MCW35OE75","created_at":"2026-07-05T10:11:02.220923+00:00"},{"alias_kind":"pith_short_8","alias_value":"V24XOQ4M","created_at":"2026-07-05T10:11:02.220923+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/V24XOQ4MCW35OE75FDUSTJ2XDR","json":"https://pith.science/pith/V24XOQ4MCW35OE75FDUSTJ2XDR.json","graph_json":"https://pith.science/api/pith-number/V24XOQ4MCW35OE75FDUSTJ2XDR/graph.json","events_json":"https://pith.science/api/pith-number/V24XOQ4MCW35OE75FDUSTJ2XDR/events.json","paper":"https://pith.science/paper/V24XOQ4M"},"agent_actions":{"view_html":"https://pith.science/pith/V24XOQ4MCW35OE75FDUSTJ2XDR","download_json":"https://pith.science/pith/V24XOQ4MCW35OE75FDUSTJ2XDR.json","view_paper":"https://pith.science/paper/V24XOQ4M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.05041&json=true","fetch_graph":"https://pith.science/api/pith-number/V24XOQ4MCW35OE75FDUSTJ2XDR/graph.json","fetch_events":"https://pith.science/api/pith-number/V24XOQ4MCW35OE75FDUSTJ2XDR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V24XOQ4MCW35OE75FDUSTJ2XDR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V24XOQ4MCW35OE75FDUSTJ2XDR/action/storage_attestation","attest_author":"https://pith.science/pith/V24XOQ4MCW35OE75FDUSTJ2XDR/action/author_attestation","sign_citation":"https://pith.science/pith/V24XOQ4MCW35OE75FDUSTJ2XDR/action/citation_signature","submit_replication":"https://pith.science/pith/V24XOQ4MCW35OE75FDUSTJ2XDR/action/replication_record"}},"created_at":"2026-07-05T10:11:02.220923+00:00","updated_at":"2026-07-05T10:11:02.220923+00:00"}