这篇论文提出了BPI指标，解决了去中心化联邦学习中拜占庭节点放置的关键问题，比传统基于节点中心性的启发式方法更准确。
研究人员提出拜占庭节点放置影响(BPI)指标，量化诚实节点在训练过程中受到拜占节点的累计暴露程度。该指标基于实际八卦动力学原理，考虑了加权多跳传播和被攻陷节点间的相互作用。研究团队开发了优化BPI的高效算法，并在六种异构图族、无目标模型投毒和后门攻击场景下进行了评估。BPI引导的放置方法能持续识别出在不同网络结构下极具破坏性的配置。
Optimizing Byzantine Node Placement in Decentralized Federated Learning
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.