Design and Development of Server-Client Cooperation Framework for Federated Learning

Federated learning is a machine learning technique that enables distributed training without explicitly data sharing between multiple heterogeneous devices. In this paper, we propose and develop a practical federated learning framework to effectively support model deployment, aggregation, and client device monitoring. The proposed approach is designed as a micro-architecture service using container-related technologies such as Docker, Kubernetes, and Prometheus.