Asynchronous Gossip Algorithms for Rank-Based Statistical Methods

2025-09-09

AI Summary

Traditional distributed algorithms (e.g., mean aggregation) lack robustness against data poisoning in decentralized AI and edge intelligence. Method: We propose the first asynchronous gossip framework for computing rank-based robust statistics in distributed settings. Our approach introduces asynchronous gossip to rank estimation—yielding the first theoretical convergence rate bound—and integrates distributed sorting, quantile estimation, and consensus protocols to support key nonparametric inference tasks, including L-statistics, rank statistics, and the Wilcoxon rank-sum test. Contribution/Results: We establish a novel paradigm for rank-based statistical inference in decentralized environments; provide the first provably convergent distributed framework for two-sample nonparametric hypothesis testing; and empirically demonstrate significantly enhanced robustness and practicality against data corruption across diverse network topologies.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Distributed Machine Learning & Federated Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Develops gossip algorithms for robust rank-based statistics
• Addresses data corruption in decentralized AI and edge intelligence
• Enables robust distributed hypothesis testing with convergence guarantees

Innovation

• Gossip algorithms for rank-based statistics
• Asynchronous decentralized robust estimation methods
• Distributed Wilcoxon rank-sum test implementation