An Interpretable Federated Learning Control Framework Design for Smart Grid Resilience

Ibrahim Shahbaz; Eman Hammad; Abdallah Farraj

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arXiv · 2511.15014

An Interpretable Federated Learning Control Framework Design for Smart Grid Resilience

Ibrahim Shahbaz·Eman Hammad·Abdallah Farraj

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Abstract

Power systems remain highly vulnerable to disturbances and cyber-attacks, underscoring the need for resilient and adaptive control strategies. In this work, we investigate a data-driven Federated Learning Control (FLC) framework for transient stability resilience under cyber-physical disturbances. The FLC employs interpretable neural controllers based on the Chebyshev Kolmogorov-Arnold Network (ChebyKAN), trained on a shared centralized control policy and deployed for distributed execution. Simulation results on the IEEE 39-bus New England system show that the proposed FLC consistently achieves faster stabilization than distributed baselines at moderate control levels (10\%--60\%), highlighting its potential as a scalable, resilient, and interpretable learning-based control solution for modern power grids.

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BibTeX RIS

Ibrahim Shahbaz, Eman Hammad, Abdallah Farraj. 2025-11-19. An Interpretable Federated Learning Control Framework Design for Smart Grid Resilience. https://arxiv.org/abs/2511.15014

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