Multi-Source Information Fusion for Anomaly Detection in Smart Grids Using Federated Learning

The wide-ranging expansion of smart grid networks has resulted in insurmountable difficulties that must be overcome to ensure the security and reliability of crucial energy infrastructures. The information system can be subjected to threats such as cyber-attacks or hardware malfunctioning resulting in a data integrity compromise which implies that the system will consequently not operate correctly. Anomaly detection methods that are relying on centralized data aggregation are problematic to the issues of data privacy and scalability resulting from such approaches. In this paper, we present a completely distinct approach that is based on federated learning that is employed in anomaly detection of smart grid networks that makes it possible to learn collaboratively in a decentralized way and in the same time protecting user privacy through connections between many grid nodes. The method integrates multi-source information fusion, incorporating smart meter readings, IoT sensor logs, and substation performance metrics to enhance anomaly detection accuracy and robustness. Tests show that the system is among the top or the best systems that have successfully identified a wide range of anomalies, have required low communication overhead, and have exhibited scalability. These findings imply that the use of federated learning presents an attractive direction for future work on the enhancement of the security and resilience of smart grid networks amidst changing threats.