The advent of sixth-generation (6G) wireless networks and cell-free massive multiple-input multiple-output (MIMO) architectures underscores the need for efficient resource allocation to support federated learning (FL) at the network edge. Existing approaches often treat communication, computation, and learning in isolation, overlooking dynamic heterogeneity and fairness, which leads to degraded performance in large-scale deployments. To address this gap, we propose a joint optimization framework that integrates communication–computation co-design, fairness-aware aggregation, and a hybrid strategy combining convex relaxation with deep reinforcement learning. Extensive experiments on benchmark vision datasets and real-world wireless traces demonstrate that the framework achieves up to 23% higher accuracy, 18% lower latency, and 21% energy savings compared with state-of-the-art baselines. These findings advance joint optimization in federated learning (FL) and demonstrate scalability for 6G applications.

With the evolution of Reconfigurable Intelligent Surface (RIS) technology, its potential for dynamically optimizing wireless channels has garnered significant attention. However, existing methods still face challenges in real-time control in complex environments due to high computational complexity. To address this, this paper proposes a reconfigurable wireless channel optimization framework based on Intelligent Metasurfaces 2.0 and designs a low-complexity control strategy. The strategy integrates an adaptive adjustment mechanism and multi-dimensional feedback, aiming to reduce system computational load. Experimental results show that compared to traditional methods (such as MRC and MMSE), the proposed method improves signal transmission quality (SNR improvement of 3.8 dB) and system stability (exponential increase to 0.92). When compared to advanced deep reinforcement learning (DRL) and graph neural network (GNN) methods, it achieves similar signal quality while reducing computational overhead by 20.0% and energy consumption by approximately 32.4%. Ablation experiments further verify the effectiveness and synergistic role of the proposed core modules. This study provides a feasible approach toward high-efficiency, low-complexity dynamic channel optimization in 5G and future communication networks.