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Federated learning (FL) has attracted growing attention since it allows forprivacy-preserving collaborative training on decentralized clients withoutexplicitly uploading sensitive data to the central server. However, recentworks have revealed that it still has the risk of exposing private data toadversaries. In this paper, we conduct reconstruction attacks and enhanceinference attacks on various datasets to better understand that sharing trainedclassification model parameters to a central server is the main problem ofprivacy leakage in FL. To tackle this problem, a privacy-preserving imagedistribution sharing scheme with GAN (PPIDSG) is proposed, which consists of ablock scrambling-based encryption algorithm, an image distribution sharingmethod, and local classification training. Specifically, our method can capturethe distribution of a target image domain which is transformed by the blockencryption algorithm, and upload generator parameters to avoid classifiersharing with negligible influence on model performance. Furthermore, we apply afeature extractor to motivate model utility and train it separately from theclassifier. The extensive experimental results and security analysesdemonstrate the superiority of our proposed scheme compared to otherstate-of-the-art defense methods. The code is available athttps://github.com/ytingma/PPIDSG.

PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning