EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning

Abstract

Split Federated Learning (SFL) is an emerging edge-friendly version of Federated Learning (FL), where clients process a small portion of the entire model. While SFL was considered to be resistant to Model Extraction Attack (MEA) by design, a recent work shows it is not necessarily the case. In general, gradient-based MEAs are not effective on a target model that is changing, as is the case in training-from-scratch applications. In this work, we propose a strong MEA during the SFL training phase. The proposed Early-Mix-GAN (EMGAN) attack effectively exploits gradient queries regardless of data assumptions. EMGAN adopts three key components to address the problem of inconsistent gradients. Specifically, it employs (i) Early-learner approach for better adaptability, (ii) Multi-GAN approach to introduce randomness in generator training to mitigate mode collapse, and (iii) ProperMix to effectively augment the limited amount of synthetic data for a better approximation of the target domain data distribution. EMGAN achieves excellent results in extracting server-side models. With only 50 training samples, EMGAN successfully extracts a 5-layer server-side model of VGG-11 on CIFAR-10, with 7% less accuracy than the target model. With zero training data, the extracted model achieves 81.3% accuracy, which is significantly better than the 45.5% accuracy of the model extracted by the SoTA method. The code is available at "https://github.com/zlijingtao/SFL-MEA".