Client Association and Resource Allocation for NOMA-Enabled Hierarchical Federated Learning With Non-IID Data

Hierarchical federated learning (HFL) provides a new client-edge-cloud paradigm to reduce the network burden brought by centralized FL. However, the current HFL framework mainly adopts orthogonal multiple access (OMA) techniques for client-edge communication, failing to cope with massive connections with limited bandwidth resources. Besides, the highly skewed data distribution across the clients can undermine the convergence, which leads to more rounds to reach the target performance. In this paper, we consider a non-orthogonal multiple access (NOMA)-enabled HFL framework that provides a spectrum-efficient approach to enable massive client connectivity. Under this framework, we investigate the joint client-edge association and resource allocation problem to optimize the dual-efficiency. We first reformulate the original problem as a bi-objective optimization problem using dynamic weights to balance the resource and data heterogeneity. After that, we formulate the edge-client association problem as a many-to-one matching game with externalities and swap the connected pairs until a stable matching is obtained. For each temporary edge-client association, we solve the CPU frequency and transmission power control problem for all clients using convex optimization methods. We conduct experiments under various unbalanced data distributions and communication environment settings. The results demonstrate that our method outperforms other benchmarks regarding client scheduling, transmission and resource allocation. © 2025 IEEE.