Open access

Regime-Aware Federated Aggregation (RAFA) for privacy-preserving energy load forecasting across heterogeneous European grid clients

Mahmoud Abbasi Alfonso González Briones Alesandro Gómez Villar Javier Curto Hernández Javier Prieto

Jul 2026 · Open Research Europe · 0 citations · 20 references

TL;DR

RAFA (Regime-Aware Federated Aggregation), a novel FL framework that explicitly detects and adapts to distribution shifts in client model updates without accessing raw client data, provides a lightweight, privacy-preserving extension of standard federated aggregation that is robust to regime shifts in energy systems.

Abstract

Background Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it attractive for privacy-sensitive energy forecasting. However, standard aggregation algorithms, most notably FedAvg and FedProx, assume stationary client data distributions, an assumption routinely violated in multi-national power grids where seasonal patterns, demand crises, and policy changes induce persistent regime shifts. Methods We present RAFA (Regime-Aware Federated Aggregation), a novel FL framework that explicitly detects and adapts to distribution shifts in client model updates without accessing raw client data. RAFA comprises three components. These are a Mahalanobis-distance detector operating on random-projected update vectors; reliability-weighted aggregation that exponentially discounts shifted clients while preserving exact FedAvg behaviour when no shift is detected; and always-on personalised head fine-tuning that adapts each client’s final prediction layer to its local regime. Results We validate RAFA on a new multi-national benchmark of six years (2019–2024) of hourly electrical load data for eight European bidding zones sourced from the ENTSO-E Transparency Platform. RAFA achieves a test MAE of 185.4 MW, an 11.8% improvement over FedAvg (210.2 MW), with by far the largest gain for the client exhibiting the strongest seasonal contrast (France, − 30.5 % ) and consistent improvements across the remaining clients (e.g., Portugal − 7.6 % , Poland − 7.4 % ). Ablation studies confirm both mechanisms contribute independently. Conclusions RAFA provides a lightweight, privacy-preserving extension of standard federated aggregation that is robust to regime shifts in energy systems. The approach generalises to any federated setting with temporally non-stationary client distributions and a separable model architecture.

DOI Semantic Scholar

Read PDF

Similar papers

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

Aug 2026

FedHAttn: an attention-based homogeneous aggregation framework for energy-efficient and accurate PM2.5 prediction in IoAP-enabled edge devices

This work proposes FedHAttn, a novel hierarchical attention–based aggregation mechanism that explicitly models inter-client model feature importance to optimize global model performance and establishes an effective aggregator that balances accuracy, robustness, and efficiency in federated PM2.5 prediction.

Sudhir Kumar, Vaneet Kour, Shivendu Mishra et al. · International Journal of Mac... · 0 citations

2026

Privacy-Preserving Consensus-Oriented Aggregation for Federated Learning With Heterogeneity

Achieving reliable network-wide consensus formation in distributed learning systems becomes increasingly challenging when edge nodes hold skewed data distributions. Federated learning (FL) enables privacy-preserving collaborative model training without sharing raw data, but statistical heterogeneity across nodes signif...

Xinyu Qu, Shaoyi Han, Erwu Liu et al. · IEEE Transactions on Network... · 1 citation

Book Open access Aug 2026

Efficient and Differentially Private Federated LLM Fine-Tuning on Heterogeneous Clients

i-FedLoRA provides privacy guarantees, improves model accuracy by up to 3.8%, and expedites training by 1.37-2.23×, and facilitates heterogeneous LoRA aggregation that selectively prioritizes high-confidence knowledge to filter DP-induced noise, thereby achieving robust knowledge transfer.

Nan Yan, Yuqing Li, Xiong Wang et al. · Proceedings of the 32nd ACM... · 0 citations

Preprint Aug 2026

Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization

Over-the-air FL with EH MDs under heterogeneous data distributions under heterogeneous data distributions is studied, and the proposed unified framework improves fairness or personalization, depending on the operating mode, while reducing communication overhead.

F. Bagci, Busra Tegin, Mohammad Kazemi et al. · 0 citations

Open access Aug 2026

APEW-Fed: adaptive privacy-aware ensemble weighting for federated anomaly detection in enterprise resource planning systems

APEW-Fed is presented, a federated ensemble framework that resolves this tension through three provably complementary mechanisms and reconciles heterogeneous score distributions across clients via differentially private quantile sketches under secure aggregation.

Adiah Qazi, Ammad Ali Khan Jadoon, Sadiqa Arshad · Scientific Reports · 0 citations