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.
@article{arxiv.2511.15014,
title = {An Interpretable Federated Learning Control Framework Design for Smart Grid Resilience},
author = {Ibrahim Shahbaz and Eman Hammad and Abdallah Farraj},
journal= {arXiv preprint arXiv:2511.15014},
year = {2025}
}