Federated Learning (FL) algorithms commonly sample a random subset of clients to address the straggler issue and improve communication efficiency. While recent works have proposed various client sampling methods, they have limitations in joint system and data heterogeneity design, which may not align with practical heterogeneous wireless networks. In this work, we advocate a new independent client sampling strategy to minimize the wall-clock training time of FL, while considering data heterogeneity and system heterogeneity in both communication and computation. We first derive a new convergence bound for non-convex loss functions with independent client sampling and then propose an adaptive bandwidth allocation scheme. Furthermore, we propose an efficient independent client sampling algorithm based on the upper bounds on the convergence rounds and the expected per-round training time, to minimize the wall-clock time of FL, while considering both the data and system heterogeneity. Experimental results under practical wireless network settings with real-world prototype demonstrate that the proposed independent sampling scheme substantially outperforms the current best sampling schemes under various training models and datasets.

提出了一种新的独立客户抽样策略，旨在最小化联合系统和数据异构设计中的FL的墙钟训练时间，同时考虑通信和计算中的数据和系统异构。通过实验结果表明，在实际无线网络环境下，所提出的独立抽样方案在各种训练模型和数据集下显著优于当前最佳的抽样方案。

自适应异构无线网络中的联邦学习与独立抽样