{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:39:26Z","timestamp":1781282366763,"version":"3.54.1"},"reference-count":27,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The detection of anomaly energy consumption patterns in smart grid metering systems remains a critical issue. This is due to data imbalance, privacy constraints, and the dynamic nature of consumption patterns. To address these concerns, we present a privacy-preserving and scalable anomaly detection framework named as FedTheftDetect framework. The proposed framework integrates deep learning algorithms into a federated learning (FL) architecture through the incorporation of advanced ensemble classifiers to detect behavioral anomalies in daily consumption patterns. A real-world smart meter dataset with significant class imbalance is used to assess the suggested framework. The dataset had significant preprocessing to identify consumption-related anomalies in behavior. Experimental results demonstrate that the suggested framework outperforms the competitive centralized and distributed models. It achieves significant improvements in Accuracy, Precision, Recall, and F1-score, all of which are close to 0.95, which indicates a great predictive capability and reliability.<\/jats:p>","DOI":"10.3390\/computers15040202","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T09:11:14Z","timestamp":1774429874000},"page":"202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FedTheftDetect: Optimizing Anomaly Detection in Smart Grid Metering Systems Using Federated Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6046-5817","authenticated-orcid":false,"given":"Samar M.","family":"Nour","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Systems, Faculty of Engineering & Technology, Badr University in Cairo (BUC), Cairo 11829, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Rady","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Systems, Faculty of Engineering & Technology, Badr University in Cairo (BUC), Cairo 11829, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed S.","family":"Hussien","sequence":"additional","affiliation":[{"name":"Egyptian Computer Emergency Readiness Team (EG-CERT), National Telecom Regulatory Authority (NTRA), Giza 12577, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sameh A.","family":"Salem","sequence":"additional","affiliation":[{"name":"Egyptian Computer Emergency Readiness Team (EG-CERT), National Telecom Regulatory Authority (NTRA), Giza 12577, Egypt"},{"name":"Department Computers and Systems Engineering, Faculty of Engineering at Helwan, Capital University (Formerly Helwan University), Cairo 11795, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samar A.","family":"Said","sequence":"additional","affiliation":[{"name":"Department Computers and Systems Engineering, Faculty of Engineering at Helwan, Capital University (Formerly Helwan University), Cairo 11795, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/s42162-024-00461-w","article-title":"Transforming the electrical grid: The role of AI in advancing smart, sustainable, and secure energy systems","volume":"8","author":"Rajaperumal","year":"2025","journal-title":"Energy Inform."},{"key":"ref_2","unstructured":"Nour, S.M., and Said, S.A. 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