Efficient Resource Allocation using Federated Learning in Cellular Networks

Abstract

Federated learning (FL) has been introduced as a promising solution to address data privacy issues. FL is receiving widespread attention in various applications, including wireless communication technology. Using FL performs local model training and uploads model parameters for global synthesis at the server. In wireless communication technology, the most concerning issue is resource allocation, when tens or hundreds of devices are involved in the operation. Therefore, this paper proposes three algorithms, random scheduling (RS), round-robin (RR), and proportional fair (PF), to allocate resources using FL with Poisson Point Process (PPP) distributed in the cellular networks. The efficiency of resource allocation is evaluated using the MNIST Fashion multi-modal dataset and considering each user's (UE) convergence speed and time.