An Interpretable Federated Learning Control Framework Design for Smart Grid Resilience
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909913345687552 |
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| author | Shahbaz, Ibrahim Hammad, Eman Farraj, Abdallah |
| author_facet | Shahbaz, Ibrahim Hammad, Eman Farraj, Abdallah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Interpretable Federated Learning Control Framework Design for Smart Grid Resilience Shahbaz, Ibrahim Hammad, Eman Farraj, Abdallah Systems and Control 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. |
| title | An Interpretable Federated Learning Control Framework Design for Smart Grid Resilience |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.15014 |