Elastic Federated Learning with Kubernetes Vertical Pod Autoscaler for edge computing

Abstract

Federated Learning (FL) is an emerging paradigm for training machine learning models across decentralized edge devices, ensuring data privacy and reducing computational tasks on the central cloud. However, the dynamic and resource-constrained nature of edge environments raises significant challenges in deploying FL applications efficiently. This paper introduces an Elastic Federated Learning framework that leverages Kubernetes Vertical Pod Autoscaler to improve the performance of FL applications. The framework enables dynamic adjustment of computational resources, facilitating effective resource utilization for model training and allowing quicker attainment of the targeted accuracy levels for the trained models. Our experiments demonstrate that the proposed framework significantly enhances FL efficiency, maintains model training progress during resource adjustments, and effectively addresses the resource allocation challenges in edge environments. This work advances the capabilities of FL in edge computing, opening doors to more efficient and scalable AI applications while preserving data privacy.