Federated learning (FL) has recently emerged as an attractive decentralized solution for wireless networks to collaboratively train a shared model while keeping data localized. As a general approach, existing FL methods tend to assume perfect knowledge of the Channel State Information (CSI) during the training phase, which may not be easy to acquire in case of fast fading channels. Moreover, literature analyses either consider a fixed number of clients participating in the training of the federated model, or simply assume that all clients operate at the maximum achievable rate to transmit model data. In this paper, we fill these gaps by proposing a training process that takes channel statistics as a bias to minimize the convergence time under imperfect CSI. Numerical experiments demonstrate that it is possible to reduce the training time by neglecting model updates from clients that cannot sustain a minimum predefined transmission rate. We also examine the trade-off between number of clients involved in the training process and model accuracy as a function of different fading regimes.

本文提出了一种训练过程，利用频道统计信息作为偏差来减小联邦机器学习模型的收敛时间，并通过数值实验证明可以通过忽略不能维持最小预定传输速率的客户端的模型更新来减少训练时间，同时研究参与训练的客户端数量与模型精度之间的权衡关系。

关于不完美CSI下无线网络联邦学习的收敛时间