Open access
Jul 2026
Privacy-preserving load forecasting in smart grids using federated learning: a comparative analysis of aggregation strategies
Experimental results on real-world energy consumption datasets demonstrate that the proposed FL framework achieves competitive forecasting accuracy while preserving client data privacy, and a rigorous comparative analysis reveals that FedProx and FedTrimmedAvg consistently outperform FedAvg under non-IID conditions.
A. Tibermacine, Ilyes Naidji, Imad Eddine Tibermacine et al.
· Frontiers in Energy Research · 1 citation