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
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...
Jia-Ying Li, Can Pei
· International Conference on... · 0 citations
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...
Jing-Ting Mei, Yang Yang, Celimuge Wu et al.
· IEEE Transactions on Cogniti... · 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 resilie...
M. Mahi, R. Naha, Alistair Barros
· International Conference on... · 0 citations
Open access
Jul 2026
Gradient-Complementary Asynchronous Federated Learning for Multi-Task Anomaly Detection in Smart Grid
A gradient-complementary asynchronous federated learning framework, which explicitly models gradient complementarity as a distributed intelligence fusion mechanism for multi-task anomaly detection and validate the effectiveness of gradient complementarity as a general computational intelligence principle for distribute...
Qin-Bo Chen, Xiang-Hua Li, Hao-Zhi Li et al.
· International Journal of Com... · 0 citations