A Survey of Integrating Federated Learning with Smart Grids: Application Prospect, Privacy Preserving and Challenges Analysis

With the widespread promotion of smart grid, the power time series data collected by smart meters also increases rapidly. How to collect these data safely and effectively, analyze and utilize them, and provide better power supply service has become a hot topic of current research. The federated learning technology has attracted much attention from researchers in recent years and various federated learning-based applications have been utilized due to its characteristics of distributed, security, encryption, and reliability. In the development of smart grids, federated learning has been applied for data analytics, privacy preserving, energy management, and so on. This paper is aimed at exploring the feasibility of applying the federated learning framework to the area of smart grids. We conclude the analysis of power time series data, discussing the tribulations and solutions in the process of privacy preserving in the smart grid, and highlighting different challenges of federated learning with the smart grid. We present a summarization among federated learning-based methods with the smart grid for a variety of purposes, with the aim to draw a comparison among federated learning-based methods in the smart grid from different aspects.