Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates need to be collected at a server. However, when being deployed at mobile edge networks, clients may have unpredictable availability and drop out of the training process, which hinders the convergence of FL. This paper tackles such a critical challenge. Specifically, we first investigate the convergence of the classical FedAvg algorithm with arbitrary client dropouts. We find that with the common choice of a decaying learning rate, FedAvg oscillates around a stationary point of the global loss function, which is caused by the divergence between the aggregated and desired central update. Motivated by this new observation, we then design a novel training algorithm named MimiC, where the server modifies each received model update based on the previous ones. The proposed modification of the received model updates mimics the imaginary central update irrespective of dropout clients. The theoretical analysis of MimiC shows that divergence between the aggregated and central update diminishes with proper learning rates, leading to its convergence. Simulation results further demonstrate that MimiC maintains stable convergence performance and learns better models than the baseline methods.

翻译：联邦学习是一种保护隐私的协作学习框架，其中模型训练任务被分配给客户端，仅需在服务器端收集模型更新。然而，当部署于移动边缘网络时，客户端可能因不可预测的可用性而退出训练过程，从而阻碍联邦学习的收敛。本文着力解决这一关键挑战。具体而言，我们首先研究了经典FedAvg算法在任意客户端丢失场景下的收敛性，发现采用常见的衰减学习率时，FedAvg会围绕全局损失函数的稳定点产生振荡，这是由于聚合更新与理想中央更新之间存在偏差。受此新发现的启发，我们设计了一种名为MimiC的新型训练算法，服务器基于先前接收的模型更新对当前更新进行修正。该修正机制无论客户端是否丢失，均能模拟虚构的中央更新。理论分析表明，通过合理设置学习率，MimiC可有效缩小聚合更新与中央更新之间的差距，从而保障收敛性。仿真结果进一步证实，MimiC能保持稳定的收敛性能，并训练出优于基准方法的模型。