Privacy-preserving Serverless Computing using Federated Learning for Smart Grids

The smart power grid is a critical energy infrastructure where real-time electricity usage data is collected to predict future energy requirements. The existing prediction models focus on the centralized frameworks, where the collected data from various Home Area Networks (HANs) are forwarded to a central server. This process leads to cybersecurity threats. This paper proposes a Federated Learning (FL) based model with privacy preservation of smart grids data using Serverless cloud computing. The model considers the Blockchain-enabled Dew Servers (BDS) in each HAN for local data storage and local model training. Advanced perturbation and normalization techniques are used to reduce the inverse impact of irregular workload on the training results. The experiment conducted on benchmarks datasets demonstrates that the proposed model minimizes the computation and communication costs, attacking probability, and improves the test accuracy. Overall, the proposed model enables smart grids with robust privacy preservation and high accuracy.