User selection has become crucial for decreasing the communication costs of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competition mechanism in random access. Taking the carrier sensing multiple access (CSMA) mechanism as an example of random access, we manipulate the contention window (CW) size to prioritize certain users for obtaining radio resources in each round of training. Training data bias is used as a target scenario for FL with user selection. Prioritization is based on the distance between the newly trained local model and the global model of the previous round. To avoid excessive contribution by certain users, a counting mechanism is used to ensure fairness. Simulations with various datasets demonstrate that this method can rapidly achieve convergence similar to that of the centralized user selection approach.

翻译：用户选择已成为降低无线网络联邦学习通信成本的关键，但集中式用户选择会带来额外的系统复杂度。本研究提出一种利用随机接入中无线资源竞争机制的网络内在分布式用户选择方法。以载波侦听多路访问机制作为随机接入实例，通过调整竞争窗口大小来优先分配无线资源给特定用户。将训练数据偏差作为联邦学习用户选择的目标场景，基于新训练的本地模型与上一轮全局模型的距离实施优先级调度。为规避特定用户过度贡献问题，引入计数机制保障公平性。基于不同数据集的仿真结果表明，该方法能快速实现与集中式用户选择方法相近的收敛性能。