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

Abstract

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.