Although Federated Learning (FL) has garnered increasing attention from researchers, the development of ad-vanced FL frameworks incorporating multiple access techniques remains relatively underexplored. This paper investigates the integration of a novel clustered federated learning (CFL) framework with non-orthogonal multiple access (NOMA) in environments with non-independent and identically distributed (non-iid) datasets. To explore the potential benefits of the proposed framework, the optimization problem is formulated as an energy minimization problem, which includes sub-channel and power allocation. The formulated problem is divided into two sub-problems, respectively solved by the matching-based algorithm and Karush-Kuhn-Tucker (KKT) conditions, in which the closed-form solution is derived. Our simulation results demon-strate that jointly optimizing sub-channel and power allocation in NOMA-enhanced networks can lead to a significant improvement in test accuracy and convergence speed in the proposed FL framework.
Sub-Channel Assignment and Power Allocation in NOMA-Enhanced Federated Learning Networks
24.06.2024
1552823 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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