提出异步传闻算法计算秩统计量，提升分布式系统抗干扰能力。
Asynchronous Gossip Algorithms for Rank-Based Statistical Methods
- 用异步传闻机制计算秩相关统计量，抗异常值能力强。
- 首次实现分布式威克逊秩和检验，收敛速度有理论保证。
- 适合边缘智能中数据不靠谱的场景，如工业传感器网络。
随着去中心化AI与边缘智能日益普及，分布式环境中的鲁棒性与可信度问题愈发突出，尤其在存在污染或恶意数据时。传统去中心化算法依赖均值等简单统计量，易受数据污染影响，亟需更稳健的统计方法。受近期关于裁剪均值与秩估计研究启发，本文提出一类用于计算广义秩统计量（包括L统计量与秩统计量）的传闻算法，这类统计量以对异常值具有鲁棒性著称。我们将该方法应用于稳健的分布式两样本假设检验，首次引入基于传闻的威克逊秩和检验算法。提供严格的收敛性保证，包括首个针对异步传闻式秩估计的收敛速率边界。通过在多种网络拓扑上的实验，验证了理论结果的有效性。
原文摘要 · Abstract (English)
As decentralized AI and edge intelligence become increasingly prevalent, ensuring robustness and trustworthiness in such distributed settings has become a critical issue-especially in the presence of corrupted or adversarial data. Traditional decentralized algorithms are vulnerable to data contamination as they typically rely on simple statistics (e.g., means or sum), motivating the need for more robust statistics. In line with recent work on decentralized estimation of trimmed means and ranks, we develop gossip algorithms for computing a broad class of rank-based statistics, including L-statistics and rank statistics-both known for their robustness to outliers. We apply our method to perform robust distributed two-sample hypothesis testing, introducing the first gossip algorithm for Wilcoxon rank-sum tests. We provide rigorous convergence guarantees, including the first convergence rate bound for asynchronous gossip-based rank estimation. We empirically validate our theoretical results through experiments on diverse network topologies.
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