GAN-Based Information Leakage Attack Detection in Federated Learning

Federated learning (FL) has been a popular distributed learning framework to reduce privacy risks by keeping private data locally. However, recent work (Hitaj 2017) has demonstrated that sharing model’s parameter updates still leaves FL vulnerable to internal attacks in its training phase. Existing works cannot detect such attacks well. To address this problem, we propose a novel and lightweight detection scheme which selects and analyzes just a few parameter updates of the last convolutional layer in the FL model. Extensive experiments demonstrate that our proposed detection scheme can accurately and efficiently detect the malicious participant in near real time for a scenario with a malicious participant.