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cs.LG2023
Theoretically Principled Federated Learning for Balancing Privacy and Utility
Xiaojin Zhang, Wenjie Li, Yiming Li +3
We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and uti…
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…