User scheduling and power allocation strategy for cell-free networks based on federated learning

In order to address the issue of limited training performance in federated learning (FL) due to user link quality disparities and imbalanced communication, and computing resource utilization in cell-free network systems, a joint optimization problem for user scheduling and power allocation was designed. Firstly, a low-complexity resource priority based secondary sampling user scheduling (RPSS-US) algorithm was proposed. Users were selected based on the availability of their computing resources and link quality, with priority given to those contributing more to system capacity and global model updates, thus improving overall training performance. Then, a power allocation algorithm based on the binary method (BM-PA) was proposed to optimize power allocation, improve user link quality differences, enhance data transmission rates, and reduce overall FL task delay. By iteratively optimizing these two sub-problems alternately, joint optimization of system performance was achieved. Simulation results demonstrate that compared to other comparison algorithms, the proposed algorithm achieves a 47.19% increase in downlink throughput, a 22.60% increase in uplink throughput, and a 57.33% reduction in FL task time consumption, while minimizing time overhead for achieving the same model accuracy