Feb. 5, 2024, 8:10 p.m. | Yi Dong Yingjie Wang Mariana Gama Mustafa A. Mustafa Geert Deconinck Xiaowei Huang

cs.CR updates on arXiv.org arxiv.org

In the realm of power systems, the increasing involvement of residential users in load forecasting applications has heightened concerns about data privacy. Specifically, the load data can inadvertently reveal the daily routines of residential users, thereby posing a risk to their property security. While federated learning (FL) has been employed to safeguard user privacy by enabling model training without the exchange of raw data, these FL models have shown vulnerabilities to emerging attack techniques, such as Deep Leakage from Gradients …

applications can cs.ai cs.cr cs.dc cs.lg cs.ma cs.sy daily data data privacy distributed eess.sy federated federated learning forecasting power power systems privacy property realm reveal risk safeguard security systems

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