Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented.

翻译：智能电网（Smart Grids, SGs）中的消费者隐私是一个主要关注点，由于能源数据的敏感性，特别是当用于训练不同服务的机器学习模型时。这些数据驱动的模型通常需要大量的数据才能实现可接受的性能，在大多数情况下会导致隐私泄露的风险。通过将训练推向边缘，联邦学习（Federated Learning, FL）在隐私保护和这些模型的预测性能之间提供了良好的折衷方案。本文介绍了FL在SGs中的应用概述，同时讨论了它们的优缺点，主要包括负荷预测、电动汽车、故障诊断、负荷分解和可再生能源等。此外，考虑数据分区、通信拓扑和安全机制，提供了主要设计趋势和可能的分类法的分析。最后，概述了面临这项技术的主要挑战和未来方向。