{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T11:04:58Z","timestamp":1756811098532,"version":"3.41.0"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Precise forecasting of photovoltaic (PV) power generation upholds flexibility and reliability within the power grid. Due to the data security dilemma of previous forecasting methods, federated learning (FL) has been widely studied for its ability to train models without sharing training data. However, the incorrect behavior from untrusted devices and servers in traditional FL frameworks can undermine the integrity of the global model, precipitating inaccurate power generation forecasting. Therefore, we propose a blockchain-enabled trusted Byzantine-robust FL framework, called TBFL, designed for decentralized and privacy-preserving PV power generation forecasting. Specifically, this framework features a trusted supervision mechanism, which can effectively eliminate malicious gradients to achieve a high-quality model. In addition, a multilevel differential privacy scheme is designed to strike a balance between privacy protection and model accuracy. Finally, a model clipping algorithm based on neuronal similarity is implemented to optimize both the duration and consumption associated with local device training. Comprehensive experimental outcomes demonstrate that the framework TBFL can successfully improve robustness, and achieve similar efficiency as FedAvg while maintaining a high forecasting accuracy.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae141","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T13:33:46Z","timestamp":1735911226000},"page":"684-696","source":"Crossref","is-referenced-by-count":1,"title":["TBFL: blockchain-enabled trusted byzantine-robust federated learning framework for photovoltaic power generation forecasting"],"prefix":"10.1093","volume":"68","author":[{"given":"Yan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power , Shanghai 201306 ,","place":["China"]}]},{"given":"Liangliang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power , Shanghai 201306 ,","place":["China"]}]},{"given":"Yiyuan","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Huizhou University , Huizhou 516007 ,","place":["China"]}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power , Shanghai 201306 ,","place":["China"]}]},{"given":"Yu","family":"Long","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrica Engineering, Shanghai Jiao Tong University , Shanghai 200240 ,","place":["China"]}]},{"given":"Kefei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Hangzhou Normal University , Hangzhou 311121 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