Cross-silo federated learning, which safeguards privacy, can identify false data injection (FDI) assaults on smart grids without compromising critical practical data. Utility companies, substations, and control centers collaborate to develop a machine learning model while maintaining the confidentiality of raw data. Consequently, each silo employs its own datasets to train the model in secrecy before transmitting encrypted or compressed updates to a central administrator or server. The solution ensures the security of user data and critical power system information while it is in the process of learning through secure aggregation, differential privacy, and homomorphic encryption. Due to its utilization of data from multiple grid locations, the federated approach facilitates the identification of FDI attacks. This allows for the identification of the initial attempts by malicious individuals to alter data, which could potentially jeopardize systems. Cross-silo federated learning is a feasible and scalable approach to safeguarding smart grid systems from intricate cyber-physical threats. In comparison to the utilization of individual models, experiments demonstrate that it enhances the efficacy of detection and safeguards privacy.
