Privacy-preserving Collaborative Learning with Scalable Image Transformation and Autoencoder

Yuting Ma, Yuanzhi Yao, Xiaowei Liu, Nenghai Yu

Abstract: Collaborative learning in which local clients jointly train a deep learning model by sharing parameters to the central- ized server has gained great popularity. However, recent works have shown that local private data can be leaked to the server by gradient sharing. In this paper, a privacy-preserving collaborative learning scheme is proposed to defend against gradient-based reconstruction attacks. The sensitive training images are firstly permutated by transformation with scalable block sizes. Then, features of permutated images are extracted by a classification- compliant autoencoder for meaningful representation of high- dimensional images and facilitating classification. The model accuracy constraint is incorporated in the training process to maintain decent classification accuracy. Experimental results demonstrate that the proposed scheme can achieve high privacy preservation with minimal impact on model accuracy.