Optimal Device Selection and Resource Allocation in Federated Learning

With the advent of federated learning, the development of privacy-preserving learning models has assumed significance in several applications. However, challenges arise due to the participation of a massive number of edge devices and limited resources in a network. In this context, this paper addresses a joint device selection and resource allocation problem to improve the performance of federated learning in resource-constrained edge networks. The proposed method enhances network performance while optimally allocating limited network resources among selected devices. The optimal device selection and resource allocation problem is formulated as maximizing the number of data samples under latency, energy, and power constraints. A computationally efficient solution to this problem is proposed to ensure an optimal solution in terms of device selection and network resources. Comparison with existing methods demonstrates its ability to find optimal solutions while significantly reducing computation time by 82%.