Fedresource

Abstract

Deep reinforcement learning can effectively deal with resource allocation (RA) in wireless networks. However, more complex networks can have slower learning speeds, and a lack of network adaptability requires new policies to be learned for newly introduced systems. To address these issues, a novel federated learning-based resource allocation (FEDRESOURCE) has been proposed in this paper which efficiently performs RA in wireless networks. The proposed FEDRESOURCE technique uses federated learning (FL) which is a ML technique that shares the DRL-based RA model between distributed systems and a cloud server to describe a policy. The regularized local loss that occurs in the network will be reduced by using a butterfly optimization technique, which increases the convergence of the FL algorithm. The suggested FL framework speeds up policy learning and allows for adoption by employing deep learning and the optimization technique. Experiments were conducted using a Python-based simulator and detailed numerical results for the wireless RA sub-problems. The theoretical results of the novel FEDRESOURCE algorithm have been validated in terms of transmission power, convergence of algorithm, throughput, and cost. The proposed FEDRESOURCE technique achieves maximum transmit power up to 27%, 55%, and 68% energy efficiency compared to Scheduling policy, Asynchronous FL framework, and Heterogeneous computation schemes respectively. The proposed FEDRESOURCE technique can increase discrimination accuracy by 1.7%, 1.2%, and 0.78% compared to the scheduling policy framework, Asynchronous FL framework, and Heterogeneous computation schemes respectively.