Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible to straggler (slower or failed) clients. Recent work has proposed to address this through device-to-device (D2D) offloading, which introduces privacy concerns. In this paper, we propose a novel straggler-optimal approach for coded matrix computations which can significantly reduce the communication delay and privacy issues introduced from D2D data transmissions in FL. Moreover, our proposed approach leads to a considerable improvement of the local computation speed when the generated data matrix is sparse. Numerical evaluations confirm the superiority of our proposed method over baseline approaches.

翻译：联邦学习（FL）是一种在分布于客户端设备上的数据上训练全局模型的流行技术。与其他分布式训练技术一样，FL容易受到掉队客户端（较慢或故障的客户端）的影响。近期研究提出通过设备到设备（D2D）卸载来解决此问题，但这引入了隐私顾虑。本文提出一种新颖的针对编码矩阵计算的掉队优化方法，能够显著减少FL中D2D数据传输带来的通信延迟和隐私问题。此外，当生成的数据矩阵稀疏时，所提方法可大幅提升本地计算速度。数值评估证实了我们的方法相较于基线方案的优越性。