Smart Grid IoT Framework Integrating Peer-to-Peer Federated Learning with Homomorphic Encryption

Homomorphic Encryption (HE) introduces new dimensions of security and privacy within federated learning (FL) and Internet of Things (IoT) frameworks that allow preservation of user privacy when handling data for FL occurring Smart Grid (SG) technologies. In this paper, we propose a novel SG IoT framework to provide a solution of predicting energy consumption while preserving user-privacy in a smart grid system. The proposed framework is based on the integration of FL, edge computing, and HE principles to provide a robust and secure framework to conduct machine learning workloads end-to-end. In the proposed framework, edge devices are connected to each other using P2P networking and the data exchanged between peers is encrypted using CKKS fully HE. The results obtained show that the system can predict energy consumption as well as preserve user privacy in SG scenarios. The findings provide an insight into the SG IoT framework that can help network researchers and engineers to contribute further towards developing a next generation SG IoT system.