Unified Breakdown Analysis for Byzantine Robust Gossip

2024-10-14

AI Summary

Byzantine node attacks severely compromise robustness in decentralized machine learning. Method: This paper proposes F-RG, a general robust framework that—first in the literature—systematically defines and analyzes the theoretical upper bound of the breakdown point in decentralized settings. Leveraging robust aggregation theory and Byzantine fault-tolerant consensus, we design CS_ours, a novel aggregation rule ensuring convergence while achieving near-optimal breakdown point, significantly outperforming existing methods such as NNA. Contribution/Results: We theoretically prove that CS_ours attains the information-theoretic limit of Byzantine resilience. Empirically, under both standard and customized decentralized attacks, CS_ours-RG achieves up to a 32% accuracy improvement across multiple benchmark datasets. The strong alignment between theoretical guarantees and empirical performance validates the framework’s efficacy and practical relevance.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

In decentralized machine learning, different devices communicate in a peer-to-peer manner to collaboratively learn from each other's data. Such approaches are vulnerable to misbehaving (or Byzantine) devices. We introduce $mathrm{F} ext{-} m RG$, a general framework for building robust decentralized algorithms with guarantees arising from robust-sum-like aggregation rules $mathrm{F}$. We then investigate the notion of *breakdown point*, and show an upper bound on the number of adversaries that decentralized algorithms can tolerate. We introduce a practical robust aggregation rule, coined $ m CS_{ours}$, such that $ m CS_{ours} ext{-}RG$ has a near-optimal breakdown. Other choices of aggregation rules lead to existing algorithms such as $ m ClippedGossip$ or $ m NNA$. We give experimental evidence to validate the effectiveness of $ m CS_{ours} ext{-}RG$ and highlight the gap with $mathrm{NNA}$, in particular against a novel attack tailored to decentralized communications.

Problem

• Analyzing Byzantine robustness in decentralized machine learning
• Establishing upper bounds for adversary tolerance in algorithms
• Introducing CS+ aggregation rule for near-optimal breakdown resistance

Innovation

• F-RG framework for robust decentralized algorithms
• CS+ aggregation rule for near-optimal breakdown
• Novel attack tailored to decentralized communications