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

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

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.

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

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 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.

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

Federated low-rank adaptation (FedLoRA) allows multiple clients to collaboratively fine-tune large language models (LLMs) on downstream tasks without exposing their private data. To mitigate privacy leakage during aggregation, differential privacy (DP) is widely used to clip and perturb local model updates with noise, yet it can compromise model accuracy due to the inherent privacy-utility trade-off. The performance degradation becomes worse under the FedLoRA setting with the amplified DP noise impact and client heterogeneity in both model structure and data distribution. In this work, we propose iP-FedLoRA, a privacy-preserving federated fine-tuning framework for heterogeneous clients that strikes a good privacy-utility balance. Specifically, to fully utilize clients' heterogeneous resources, we customize LoRA modules based on their available resources. iP-FedLoRA employs matrix-wise differentially private local fine-tuning with sensitivity-aware noise allocation and rank-compensated LoRA regularization, which effectively alleviates noise impact of low-rank modules and enhances training efficiency. By leveraging noise-resilient knowledge distillation, iP-FedLoRA facilitates heterogeneous LoRA aggregation that selectively prioritizes high-confidence knowledge to filter DP-induced noise, thereby achieving robust knowledge transfer. Through rigorous privacy analysis and extensive experiments, we show that iP-FedLoRA provides privacy guarantees, improves model accuracy by up to 3.8%, and expedites training by 1.37-2.23×.

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

Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability. In practical wireless networks, mobile devices (MDs) often exhibit diverse data and learning objectives, naturally forming clusters of users with jointly trainable models. When devices rely on energy harvesting (EH), stochastic energy arrivals further complicate participation and scheduling under communication constraints. In this work, we study over-the-air (OTA) FL with EH MDs under heterogeneous data distributions, and investigate two closely related learning objectives within a unified framework: one aiming for a more representative global model by reducing data bias, and the other learning more personalized cluster-specific models by exploiting this bias. In the global training mode, cluster information guides energy- and diversity-aware scheduling, ensuring that the scheduled active users provide a more representative aggregate update. In the personalization mode, the same cluster structure defines cluster-level learning objectives and OTA recovery targets, enabling the parameter server to train multiple cluster-specific models through simultaneous transmissions over the wireless multiple-access channel. Numerical results demonstrate that the proposed unified framework improves fairness or personalization, depending on the operating mode, while reducing communication overhead.

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

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.