论文标题
能源系统中联邦学习的评论
A Review of Federated Learning in Energy Systems
论文作者
论文摘要
随着对数据隐私和所有权的越来越关注，近年来见证了机器学习的范式转移（ML）。新兴的范式，联合学习（FL）引起了极大的关注，并已成为机器学习实现的新设计。 FL在中央服务器的协调下启用数据筒仓的ML模型培训，从而消除了开销的通信，而无需共享原始数据。在本文中，我们对FL范式进行了综述，尤其是比较了网络结构和全局模型聚合方法。然后，我们对能源域中的FL应用进行了全面审查（请参阅本文中的智能电网）。我们提供了FL的主题分类，以解决各种与能源有关的问题，包括需求响应，识别，预测和联合优化。我们详细描述了分类法，并通过讨论各个方面的讨论，包括其能源信息学应用程序中的挑战，机会和局限性，例如能源系统建模和设计，隐私和进化。
With increasing concerns for data privacy and ownership, recent years have witnessed a paradigm shift in machine learning (ML). An emerging paradigm, federated learning (FL), has gained great attention and has become a novel design for machine learning implementations. FL enables the ML model training at data silos under the coordination of a central server, eliminating communication overhead and without sharing raw data. In this paper, we conduct a review of the FL paradigm and, in particular, compare the types, the network structures, and the global model aggregation methods. Then, we conducted a comprehensive review of FL applications in the energy domain (refer to the smart grid in this paper). We provide a thematic classification of FL to address a variety of energy-related problems, including demand response, identification, prediction, and federated optimizations. We describe the taxonomy in detail and conclude with a discussion of various aspects, including challenges, opportunities, and limitations in its energy informatics applications, such as energy system modeling and design, privacy, and evolution.
