Matching Theory Aided Federated Learning Method for Load Forecasting of Virtual Power Plant

Abstract

As an emerging distributed learning paradigm, Federated Learning (FL) allows smart meters to collaboratively train a load forecasting model while keeping their private data on local devices. However, two critical issues hinder the deployment of ordinary FL algorithm in load forecasting: (i) one global model cannot fit all users well due to their heterogeneous load patterns; (ii) the training speed of FL severely depends on a few stragglers with scarce communication and computing resources. In this work, we propose a novel multi-center FL framework for load forecasting to learn multiple models simultaneously by grouping the users according to their model dissimilarity and training time. Specifically, a problem is formulated to jointly optimize the grouping strategy and forecasting model parameters, which is resolved by integrating the matching algorithm into the update process of model parameters in FL. Simulation results on real load data show that, compared with the existing load forecasting methods based on FL, the prediction error of our scheme is reduced by 8.11%, and the training time is reduced by 90.37%.