A Federated-Learning Algorithm Based on Client Sampling and Gradient Projection for the Smart Grid

Abstract

Federated learning (FL) is a machine-learning framework that effectively addresses privacy concerns. It harnesses fragmented data from devices across the globe for model training and optimization while strictly adhering to user privacy protection and regulatory compliance. This framework holds immense potential for widespread applications in the smart-grid domain. Through FL, power companies can collaborate to train smart-grid models without revealing users’ electricity consumption data, thus safeguarding their privacy. However, the data collected by clients often exhibits heterogeneity, which can lead to biases towards certain data features during the model-training process, therefore affecting the fairness and performance of the model. To tackle the fairness challenges that emerge during the federated-learning process in smart grids, this paper introduces FedCSGP, a novel federated-learning approach that incorporates client sampling and gradient projection. The main idea of FedCSGP is to categorize the causes of unfairness in federated learning into two parts: internal conflicts and external conflicts. Among them, the client-sampling strategy is used to resolve external conflicts, while the gradient-projection strategy is employed to address internal conflicts. By tackling both aspects, FederCSGP aims to enhance the fairness of the federated-learning model while ensuring the accuracy of the global model. The experimental results demonstrate that the proposed method significantly improves the accuracy of poorly performing clients in smart-grid scenarios with lower communication costs, therefore enhancing the fairness of the federated-learning algorithm.