Towards Convergence in Federated Learning via Non-IID Analysis in a Distributed Solar Energy Grid

Abstract

Federated Learning (FL) is an effective framework for a distributed system that constructs a powerful global deep learning model, which diminishes the local bias and accommodates the successful aggregation of locally trained models with heterogeneous datasets. However, when local datasets have the non-IID attribute, the optimization metric tends to diverge or show unstable convergence in the trajectory space. This paper delves into building a global model for the distributed Smart Grid environment, with regionally cumulated three solar energy datasets from January 2017 to August 2021 in a decentralized power grid in South Korea via FL. This distributed energy network involves local properties and physical distance between the regions, which raises a fundamental question of “Will time-serially curated non-IID local features be effective in constructing a global regression model?”. This paper probes this question by leveraging FL and conducts the theoretically viable non-IID case-by-case convergence analysis, providing the interpretation of the embedded temporal non-IID features and application on real-world data. Moreover, most of the FL studies predetermine the global update period, which lacks applicability when adapting FL in actual practice. As FL is a cumulative-basis structure, the update term is a crucial factor that needs to be carefully selected. This paper articulates this problem and explores the effective update period via multiple experiments on the 4.5 years of solar energy dataset, and to the best of my knowledge, this is the first literature that presents the optimal update period in the FL regression in an energy domain.