Meta-heuristic federated learning aggregation methods for load forecasting

Abstract

Federated learning (FL) is essential to energy transition as it leverages decentralized energy data and machine learning to collaborative train global energy predictive models across distributed energy resources while preserving data privacy. This paper introduces one of the first FL frameworks that efficiently integrates swarm intelligence-based aggregation methods to large-scale energy consumption forecasting, by extending the TensorFlow Federated Core framework with specialized functional enhancements. The primary objective is to enhance forecasting accuracy in decentralized learning settings. We investigated the effectiveness of various nature-inspired metaheuristics for optimizing the aggregation of local model updates from distributed energy resource nodes into a global model for load forecasting tasks, including Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) against the standard Federated Averaging (FedAvg) algorithm. Using a real-world dataset comprising of 4,438 distinct energy consumers, we demonstrate that metaheuristic aggregators consistently outperform the most well-known method, Federated Averaging in predictive accuracy. Among these approaches, GWO emerges as the superior performer achieving up to 23.6% error reduction. Our findings underscore the significant potential of metaheuristic-based aggregation mechanisms in improving FL outcomes, particularly in energy forecasting applications involving large-scale distributed data scenarios.