Optimized Device Selection and Resource Management Framework for Federated Learning

Federated learning has emerged as an effective approach for building a unified machine learning model by training across multiple edge devices without sharing data, thus ensuring privacy. However, a large number of participating devices intensifies competition for limited network resources, making it challenging to include all devices in the training process. Selecting an optimal set of devices while managing resources is essential for maintaining the framework's resilience. In this context, this paper focuses on maximizing device participation while efficiently managing limited uplink transmission power and bandwidth under constrained latency. Through comprehensive mathematical analysis, we propose an optimized device selection and resource management method that achieves the optimal solution with reduced computational time. Experimental results show that the proposed method effectively identifies the optimal set of devices and resources while reducing computational time by a factor of 44.41 compared to existing state-of-the-art methods.