Federated Learning (FL) enables collaborative model training across distributed clients without data sharing, but its high computational and communication demands strain resource-constrained devices. While existing methods use dynamic pruning to improve efficiency by periodically adjusting sparse model topologies while maintaining sparsity, these approaches suffer from issues such as greedy adjustments, unstable topologies, and communication inefficiency, resulting in less robust models and suboptimal performance under data heterogeneity and partial client availability. To address these challenges, we propose Federated Robust pruning via combinatorial Thompson Sampling (FedRTS), a novel framework designed to develop robust sparse models. FedRTS enhances robustness and performance through its Thompson Sampling-based Adjustment (TSAdj) mechanism, which uses probabilistic decisions informed by stable, farsighted information instead of deterministic decisions reliant on unstable and myopic information in previous methods. Extensive experiments demonstrate that FedRTS achieves state-of-the-art performance in computer vision and natural language processing tasks while reducing communication costs, particularly excelling in scenarios with heterogeneous data distributions and partial client participation. Our codes are available at: https://github.com/Little0o0/FedRTS

本研究针对联邦学习中高计算和通信需求对资源有限设备的挑战，提出了一种新颖的联邦鲁棒剪枝框架（FedRTS）。该方法通过基于汤普森采样的调整机制，利用稳定和前瞻性的信息作出决策，从而提高鲁棒性和性能。实验结果表明，FedRTS在计算机视觉和自然语言处理任务上表现出色，同时降低了通信成本，特别是在数据分布异构和部分客户端参与的场景中。 

联邦鲁棒剪枝：基于组合汤普森采样的方法