# An Interpretable Federated Learning Control Framework Design for Smart Grid Resilience

arXiv: https://arxiv.org/abs/2511.15014 · HTML: https://mlsanity.com/paper/2511.15014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2025.

## Built on by (most cited first, 2 of 2)

- [Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids](https://mlsanity.com/paper/2607.05553/) (2026)
- [Engineering Trustworthy Agentic AI for Critical Systems](https://mlsanity.com/paper/2607.18548/) (2026)

## Abstract (arXiv)

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.