Author

Ilyes Naidji

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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