  This study explores the benefits of integrating the novel clustered federated
learning (CFL) approach with non-orthogonal multiple access (NOMA) under
non-independent and identically distributed (non-IID) datasets, where multiple
devices participate in the aggregation with time limitations and a finite
number of sub-channels. A detailed theoretical analysis of the generalization
gap that measures the degree of non-IID in the data distribution is presented.
Following that, solutions to address the challenges posed by non-IID conditions
are proposed with the analysis of the properties. Specifically, users' data
distributions are parameterized as concentration parameters and grouped using
spectral clustering, with Dirichlet distribution serving as the prior. The
investigation into the generalization gap and convergence rate guides the
design of sub-channel assignments through the matching-based algorithm, and the
power allocation is achieved by Karush-Kuhn-Tucker (KKT) conditions with the
derived closed-form solution. The extensive simulation results show that the
proposed cluster-based FL framework can outperform FL baselines in terms of
both test accuracy and convergence rate. Moreover, jointly optimizing
sub-channel and power allocation in NOMA-enhanced networks can lead to a
significant improvement.
