{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T07:47:01Z","timestamp":1768204021467,"version":"3.49.0"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172142"],"award-info":[{"award-number":["62172142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In the context of security protection against false data injection attacks (FDIAs) in power grids, traditional federated learning effectively utilizes decentralized data resources for distributed training and achieves global collaboration. However, during the model aggregation process, it often overlooks or drowns out local sparse key features, significantly increasing the risk of missed detection of specific attack patterns. To address this issue, this paper proposes a personalized detection framework based on federated learning. Initially, the bidirectional transformer detection (BTD) model detection algorithm is deployed on the client side and trained on local data. Subsequently, through personalized federated learning, the client dynamically combines the weights of the global and local models to generate a personalized detection model. The framework employs a collaborative optimization mechanism of \u201cglobal knowledge sharing and local feature adaptation\u201d to effectively mitigate the feature drowning problem while strictly safeguarding data privacy. Compared to existing methods, this approach significantly enhances detection accuracy and robustness against differentiated attack patterns, thereby establishing a more reliable security defense system for smart grids.<\/jats:p>","DOI":"10.1002\/cpe.70543","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T17:37:10Z","timestamp":1767807430000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Personalized Federated Learning for Detecting False Data Injection Attacks in Power Grids"],"prefix":"10.1002","volume":"38","author":[{"given":"Mengwei","family":"Lv","sequence":"first","affiliation":[{"name":"School of Information Engineering Henan University of Science and Technology  Luoyang Henan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruijuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information Engineering Henan University 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China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4361-8604","authenticated-orcid":false,"given":"Xuhui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Engineering Henan University of Science and Technology  Luoyang Henan 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