Problem
Research questions and friction points this paper is trying to address.
Byzantine Node Placement
Decentralized Federated Learning
Security Evaluation
Malicious Influence
Communication Graph
Optimizing Byzantine Node Placement in Decentralized Federated Learning
🤖 AI Summary
研究通过引入Byzantine Placement Influence (BPI)方法，优化拜占庭节点在去中心化联邦学习中的位置选择，以最大化恶意影响。
📝 Abstract
Security evaluations of decentralized federated learning (DFL) typically focus on how Byzantine participants behave, while largely overlooking which participants are compromised. Yet, because aggregation is distributed over a communication graph, the placement of Byzantine nodes determines how malicious influence propagates through the network. We therefore treat Byzantine placement as an explicit adversarial decision and formulate the attacker's objective as selecting, under a fixed compromise budget, the set of participants that maximizes its finite-time impact on honest nodes. To approximate this objective without executing the learning process for every candidate placement, we introduce Byzantine Placement Influence (BPI), a set-level measure derived from the actual gossip dynamics that quantifies the cumulative exposure of honest nodes to Byzantine sources over the training horizon. Unlike placement criteria based on node centrality heuristics, BPI directly accounts for weighted multi-hop propagation and interactions among compromised nodes. We develop efficient algorithms for optimizing BPI and evaluate them across six heterogeneous graph families, untargeted model poisoning, and backdoor attacks. BPI-guided placements consistently identify highly damaging configurations across different network structures and remain effective when the linear gossip assumption is relaxed through Byzantine-robust aggregation. Our results show that Byzantine placement is a critical but under-modeled dimension of DFL threat models and robustness evaluations.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Byzantine Placement Influence (BPI)
decentralized federated learning
adversarial decision
weighted multi-hop propagation
robustness evaluations
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