联邦学习与智能电网的交叉路口：概述、挑战和展望（英文版）

英文标题：Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives中文摘要：本文旨在讨论在智能电网（SGs）中使用联邦学习（FL）的优势、局限性、设计趋势和挑战。而这一技术主要应用在负载预测、电动汽车、故障诊断、负载分解和可再生能源等方面，目的在于保护用户隐私和提高数据分析的准确性。英文摘要：Consumer's privacy is a main concern in Smart Grids (SGs) due to thesensitivity of energy data, particularly when used to train machine learningmodels for different services. These data-driven models often require hugeamounts of data to achieve acceptable performance leading in most case