DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning

• URL: http://arxiv.org/abs/2305.01267v1
• Date: Tue, 2 May 2023 09:04:34 GMT
• Title: DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning
• Authors: Wenqiang Sun, Sen Li, Yuchang Sun, Jun Zhang
• Abstract summary: Federated learning (FL) attempts to train a global model by aggregating local models from distributed devices under the coordination of a central server. The existence of a large number of heterogeneous devices makes FL vulnerable to various attacks, especially the stealthy backdoor attack. 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.
• Score: 14.312593000209693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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