Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

TL;DR

This paper reviews how federated learning enhances privacy in smart grids by enabling decentralized data training, discusses current applications, challenges, and future research directions in this emerging field.

Contribution

It provides a comprehensive overview of federated learning applications in smart grids, analyzing design trends, challenges, and proposing future perspectives.

Findings

Federated learning improves privacy in smart grid data analysis.

Applications include load forecasting, electric vehicles, fault diagnosis, and renewable energy management.

Main challenges involve data partitioning, communication, and security mechanisms.

Abstract

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.