Federated Learning (FL) is widely recognized as a privacy-preserving machine learning paradigm due to its model-sharing mechanism that avoids direct data exchange. However, model training inevitably leaves exploitable traces that can be used to infer sensitive information. In Decentralized FL (DFL), the overlay topology significantly influences its models' convergence, robustness, and security. This study explores the feasibility of inferring the overlay topology of DFL systems based solely on model behavior, introducing a novel Topology Inference Attack. A taxonomy of topology inference attacks is proposed, categorizing them by the attacker's capabilities and knowledge. Practical attack strategies are developed for different scenarios, and quantitative experiments are conducted to identify key factors influencing the attack effectiveness. Experimental results demonstrate that analyzing only the public models of individual nodes can accurately infer the DFL topology, underscoring the risk of sensitive information leakage in DFL systems. This finding offers valuable insights for improving privacy preservation in decentralized learning environments.

该研究解决了去中心化联邦学习（DFL）中模型行为如何影响信息泄露的问题。提出了一种新颖的拓扑推断攻击方法，能够仅基于模型行为推断DFL系统的拓扑结构。在实验中发现，仅分析个别节点的公共模型就能准确推断DFL拓扑，这表明去中心化学习环境中存在敏感信息泄露的潜在风险，为隐私保护的改进提供了重要见解。

从模型到网络拓扑：去中心化联邦学习中的拓扑推断攻击