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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. · 1 citation
#federated learning Conference Aug 2026

Federated learning-based privacy-preserving multisource data fusion for smart grids

This study addresses the challenges of privacy leakage and data silos in multi-source heterogeneous data interaction within smart grids by designing a federated multi-source data fusion architecture that combines adaptive local differential privacy with feature space alignment. This architecture utilizes Hessian matrix trace perception to adaptively adjust the local noise budget and introduces a dynamic aggregation selection mechanism based on the maximum mean difference, reconciling the conflict between differential privacy perturbations and feature manifold losses. Experimental results show that, while ensuring strict differential privacy boundaries, the system improves test accuracy by 7.45%, achieves a model inference speed of 45 FPS, and reduces communication resource overhead by 36.5%. Even under extreme conditions such as nonindependent identically distributed skew and 15% Byzantine poisoning attacks, it maintains a 98.40% attack interception rate and robust generalization fusion performance, providing a feasible system solution for building a highly reliable and resilient situational awareness and control foundation for the distribution IoT.

Jiaying Li, Can Pei · 0 citations
#machine learning Preprint Aug 2026

Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization

This paper studies the role of model initialization in federated STLF, and proposes two initialization strategies from global and local perspectives, which effectively improve forecasting performance, as evidenced by reduced client drift, improved convergence behavior, and lower forecasting errors.

Jia-Ning Chen, Vajiheh Farhadi, Yan Li et al. · 0 citations
2026

Dynamic Weighting and Adaptive Sparse Transformer for Federated Fault Diagnosis

With the rapid development of the Internet of Things (IoT) and edge computing, the scale and complexity of modern networks have increased significantly, driving the demand for distributed fault diagnosis. Federated learning (FL) effectively addresses the issues of data privacy and dispersion by enabling edge devices to collaboratively train models without sharing raw data. However, existing FL-based fault diagnosis methods still encounter the following challenges. Firstly, static aggregation strategies struggle to balance the contributions of heterogeneous clients dynamically. Secondly, traditional local models are unable to effectively decouple sparse high and low-frequency features in fault signals, thereby limiting the accuracy of fault identification. Finally, the resource constraints of edge devices restrict the deployment of complex diagnostic models. To address these challenges, we propose a federated learning hybrid dynamic weight adjustment method based on delay and model quality, introducing the concept of “accelerated depreciation” in accounting and taxation and the concept of “asset allocation” in economics to improve the communication efficiency of fault diagnosis and reduce the impact of delay differences due to device heterogeneity on the effect of fault diagnosis. Additionally, we propose an adaptive sparse low high frequencies Transformer, introducing a lightweight attention mechanism and an adaptive feature extraction layer, which significantly reduces the computational overhead while maintaining high diagnostic accuracy. The experimental results show that, compared with the most competitive baseline, our method improves the fault diagnosis accuracy by 1% on the Case Western Reserve University Bearing dataset (CWRU), 0.78% on the Xi’an Jiaotong University Gearbox dataset (XJTU), and 0.44% on the Micro service Edge Computing dataset (MICRO).

Jingting Mei, Yang Yang, Celimuge Wu et al. · 0 citations
Conference Jul 2026

A Causal-Driven Hierarchical Decentralised Federated Learning Framework for Resilient Load Forecasting in Distributed Microgrids

Modern microgrids require distributed intelligence and edge computing to handle variable demand and renewable generation, but heterogeneity, communication limits, and privacy hinder centralised forecasting. This paper proposes a causally guided hierarchical decentralised federated learning (H-DFL) framework for resilient short-term load forecasting, integrating a hybrid TCN–BiLSTM with MCMC-based probabilistic causal feature selection. A three-tier architecture enables local training and hierarchical aggregation without raw data sharing, improving scalability and communication efficiency through sparse, interpretable feature selection driven by key factors such as solar and weather dynamics. Experiments on the Ausgrid dataset show improved stability and efficiency over Granger Causality (GC), Dynamic Causal Modeling (DCM), and Markov chain Monte Carlo (MCMC) baselines, with intervention tests confirming robustness under solar, demand, and outage disturbances. Overall, the results demonstrate that combining probabilistic feature sparsity with hierarchical decentralised federated learning to enable scalable, privacy-preserving, and resilient load forecasting for future microgrid systems.

M. Mahi, R. Naha, Alistair Barros · 0 citations
Open access Jul 2026

Gradient-Complementary Asynchronous Federated Learning for Multi-Task Anomaly Detection in Smart Grid

The increasing deployment of heterogeneous IoT devices has transformed smart grids into large-scale distributed cyber–physical systems, where anomaly detection becomes a critical yet challenging computational intelligence problem. In such environments, anomaly knowledge is sparse, fragmented, and highly non-independent across users, while device participation is asynchronous and communication-constrained. This paper proposes a gradient-complementary asynchronous federated learning (GC-AFL) framework, which explicitly models gradient complementarity as a distributed intelligence fusion mechanism for multi-task anomaly detection. Unlike conventional federated aggregation that suppresses heterogeneity, GC-AFL exploits dissimilar gradient information to preserve task-specific anomaly knowledge. The framework further integrates a communication-aware collaboration strategy and a staleness-compensated aggregation scheme to ensure efficiency and long-term fairness under asynchronous updates. Extensive experiments demonstrate that GC-AFL consistently outperforms state-of-the-art synchronous and asynchronous federated learning methods in terms of detection accuracy, robustness to Non-IID data, anomaly recall, and communication efficiency. The results validate the effectiveness of gradient complementarity as a general computational intelligence principle for distributed anomaly detection.

Qin-Bo Chen, Xiang-Hua Li, Hao-Zhi Li et al. · 0 citations

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