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.
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
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
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 significantly degrades convergence and may expose sensitive label statistics to distribution-inference attacks. To address these limitations, this paper presents a new federated consensus-oriented aggregation (FedCOA) strategy, which improves consensus formation of FL under strong heterogeneity, suppresses skew-induced instability while mitigating distribution-level privacy leakage. FedCOA perturbs local label distributions using differential privacy (DP) and computes a noise-robust Index of Data Heterogeneity (IDH), which guides dynamic aggregation, regulates bias propagation, suppresses the influence of skewed updates, and facilitates consensus formation dynamics. We show theoretically that FedCOA reduces the divergence term in the convergence upper bound. Experiments demonstrate up to 80.3%, 75.1%, and 79.2% reductions in communication rounds on MNIST, FashionMNIST, and CIFAR-10, respectively.
Xinyu Qu, Shaoyi Han, Erwu Liu et al.· IEEE Transactions on Network...· 1 citation
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
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
Enterprise Resource Planning (ERP) systems are high-value targets for fraud owing to the sensitive financial and operational data they process. Anomaly detection in such systems faces a fundamental tension: centralising data for accurate detection conflicts with privacy regulations such as the European Union’s General Data Protection Regulation (GDPR) and the United States’ Sarbanes–Oxley Act (SOX). We present APEW-Fed, a federated ensemble framework that resolves this tension through three provably complementary mechanisms. First, Adaptive Privacy-Aware Ensemble Weighting (APEW) frames model fusion as a privacy-penalised optimisation problem and derives closed-form dynamic weights that up-weight privacy-efficient, high-confidence detectors. Second, Feature-Sensitivity Calibrated Differential Privacy (FS-CDP) partitions features into sensitivity tiers and allocates per-tier Gaussian noise budgets that provably minimise total noise variance subject to a global Rényi DP constraint. Third, Federated Anomaly Score Calibration (FASC) reconciles heterogeneous score distributions across clients via differentially private quantile sketches under secure aggregation. Integrating Isolation Forest, a federated autoencoder trained with per-step DP-SGD, adaptive DBSCAN, and gradient boosting, we evaluate on 243,531 real-world ERP transactions with 5000 expert-annotated anomalies and validate on two public benchmarks: the IEEE-CIS Fraud Detection dataset and the NSL-KDD network intrusion dataset. APEW-Fed achieves 93.5% F1-score at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\alpha ,\epsilon )=(8,1.0)$$\end{document} RDP—only 2.7 percentage points below the non-private centralised baseline—while reducing communication cost by 94% versus FedAvg. On a strictly unbiased uniform-only evaluation subset (2500 transactions, no score-based enrichment), APEW-Fed achieves 92.1% F1, confirming that the headline result is not an artefact of the annotation sampling design. Membership inference attack success is 51.2%, near-random guessing, confirming strong empirical privacy.