Asynchronous Gossip Algorithms for Rank-Based Statistical Methods
This addresses robustness in distributed AI for applications like edge intelligence, though it is incremental as it builds on prior work on decentralized trimmed means and ranks.
The paper tackled the problem of robust decentralized AI in adversarial settings by developing asynchronous gossip algorithms for computing rank-based statistics, achieving rigorous convergence guarantees and introducing the first gossip algorithm for Wilcoxon rank-sum tests.
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.