From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning
Federated Learning (FL) is widely recognized as a privacy-preserving machine learning paradigm due to its model-sharing mechanism that avoids direct data exchange. Nevertheless, model training leaves exploitable traces that can be used to infer sensitive information. In Decentralized FL (DFL), the topology, defining how participants are connected, plays a crucial role in shaping the model's privacy, robustness, and convergence. However, the topology introduces an unexplored vulnerability: attackers can exploit it to infer participant relationships and launch targeted attacks. This work uncovers the hidden risks of DFL topologies by proposing a novel Topology Inference Attack that infers the topology solely from model behavior. A taxonomy of topology inference attacks is introduced, categorizing them by the attacker's capabilities and knowledge. Practical attack strategies are designed for various scenarios, and experiments are conducted to identify key factors influencing attack success. The results demonstrate that analyzing only the model of each node can accurately infer the DFL topology, highlighting a critical privacy risk in DFL systems. These findings offer valuable insights for improving privacy preservation in DFL environments.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningInference AttackPrivacy PreservingSimilar Papers 제목 키워드 기반
Stabilizing Decentralized Federated Fine-Tuning via Topology-Aware Alternating LoRA
Decentralized federated learning (DFL), a serverless variant of federated learning, poses unique challenges for parameter-efficient fine-tuning due to the factorized structure of low-rank adaptation (LoRA). Unlike linear…
parameter-efficient fine-tuningFederated LearningAir-Plan: Query-Optimized Topology Selection for Over-the-Air Decentralized Federated Learning
Over-the-air (OTA) aggregation exploits the superposition property of wireless multiple-access channels to aggregate model updates from multiple devices within a single transmission slot, significantly reducing communica…
Federated LearningBeyond Passive Aggregation: Active Auditing and Topology-Aware Defense in Decentralized Federated Learning
Decentralized Federated Learning (DFL) remains highly vulnerable to adaptive backdoor attacks designed to bypass traditional passive defense metrics. To address this limitation, we shift the defensive paradigm toward a n…
Federated LearningImpact of Network Topology on Byzantine Resilience in Decentralized Federated Learning
Federated learning (FL) enables a collaborative environment for training machine learning models without sharing training data between users. This is typically achieved by aggregating model gradients on a central server.…
Federated LearningDFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Decentralized Federated Learning (DFL) has emerged as a robust distributed paradigm that circumvents the single-point-of-failure and communication bottleneck risks of centralized architectures. However, a significant cha…
Federated LearningGraph Learning