Privacy-preserving load forecasting in smart grids using federated learning: a comparative analysis of aggregation strategies

Abstract

Accurate load forecasting plays a vital role in optimizing energy distribution and integrating renewable energy sources within smart grid systems. However, traditional centralized deep learning approaches present major challenges related to data privacy, communication overhead, and scalability, particularly in scenarios involving distributed energy consumers. To address these concerns, this study proposes a federated learning (FL) framework that leverages Gated Recurrent Unit (GRU) networks to enable decentralized and privacy-preserving load forecasting. The proposed approach is evaluated using three distinct aggregation strategies: Federated Averaging (FedAvg), Federated Proximal (FedProx), and Federated Averaging with Trimmed Mean (FedTrimmedAvg). These methods aim to alleviate data heterogeneity and client drift, which are prevalent in non-independent and identically distributed (non-IID) settings commonly encountered in smart grid environments—challenges known to limit the effectiveness of standard FedAvg and that motivate the need for more robust alternatives. Experimental results on real-world energy consumption datasets demonstrate that the proposed FL framework achieves competitive forecasting accuracy while preserving client data privacy. A rigorous comparative analysis reveals that FedProx and FedTrimmedAvg consistently outperform FedAvg under non-IID conditions, with FedTrimmedAvg offering the highest robustness to outliers and inconsistent client behavior. These findings highlight the effectiveness of robust aggregation techniques in federated settings and present a scalable, privacy-aware solution for intelligent energy management in next-generation smart grid infrastructures.