Privacy-preserving federated learning based on noise addition

Xianlin Wu, Yuwen Chen, Haiyang Yu, Zhen Yang

Abstract: Highlights•The directions of noise are optimized in the proposed noise addition approach.•The proposed approach outperforms conventional methods while offers higher accuracy.•The proposed noise addition approach can resist gradient inversion attacks.