Enhancing Model Privacy in Federated Learning with Random Masking and Quantization
By: Zhibo Xu , Jianhao Zhu , Jingwen Xu and more
Potential Business Impact:
Keeps computer learning private while still working well.
Experimental results across various models and tasks demonstrate that our approach not only maintains strong model performance in federated learning settings but also achieves enhanced protection of model parameters compared to baseline methods.
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