DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning
Federated learning (FL) attempts to train a global model by aggregating local models from distributed devices under the coordination of a central server. However, the existence of a large number of heterogeneous devices makes FL vulnerable to various attacks, especially the stealthy backdoor attack. Backdoor attack aims to trick a neural network to misclassify data to a target label by injecting specific triggers while keeping correct predictions on original training data. Existing works focus on client-side attacks which try to poison the global model by modifying the local datasets. In this work, we propose a new attack model for FL, namely Data-Agnostic Backdoor attack at the Server (DABS), where the server directly modifies the global model to backdoor an FL system. Extensive simulation results show that this attack scheme achieves a higher attack success rate compared with baseline methods while maintaining normal accuracy on the clean data.
Code (0)
등록된 구현이 없습니다.
Tasks
Backdoor AttackFederated LearningSimilar Papers 제목 키워드 기반
FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks
Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to …
backdoor defenseFederated LearningCooperative Decentralized Backdoor Attacks on Vertical Federated Learning
Federated learning (FL) is vulnerable to backdoor attacks, where adversaries alter model behavior on target classification labels by embedding triggers into data samples. While these attacks have received considerable at…
Backdoor AttackFederated LearningMetric LearningVertical Federated LearningOn Feasibility of Server-side Backdoor Attacks on Split Learning
Split learning is a collaborative learning design that allows several participants (clients) to train a shared model while keeping their datasets private. Recent studies demonstrate that collaborative learning models, sp…
Backdoor AttackFederated LearningDABS: A Domain-Agnostic Benchmark for Self-Supervised Learning
Self-supervised learning algorithms, including BERT and SimCLR, have enabled significant strides in fields like natural language processing, computer vision, and speech processing. However, these algorithms are domain-sp…
Self-Supervised LearningSafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)
Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without requiring clients to share their private…
Federated Learning