2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning
Xue Yang, Depan Peng, Yan Feng +3
Bidirectional privacy-preservation federated learning is crucial as both local gradients and the global model may leak privacy. However, only a few works attempt to achieve it, and…
cs.LG2020★ 2 cited
An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning
Xue Yang, Yan Feng, Weijun Fang +4
Although federated learning improves privacy of training data by exchanging local gradients or parameters rather than raw data, the adversary still can leverage local gradients and…