3 papers
cs.LG2025
Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning
Tiandi Ye, Wenyan Liu, Kai Yao +6
Federated learning (FL) is a privacy-preserving machine learning paradigm that enables collaborative model training across multiple distributed clients without disclosing their raw…
cs.LG2025
Privacy-Preserving Inference for Quantized BERT Models
Tianpei Lu, Bingsheng Zhang, Lekun Peng +3
With the increasing deployment of generative machine learning models in privacy-sensitive domains such as healthcare and personalized services, ensuring secure inference has become…
cs.CR2024
The Communication-Friendly Privacy-Preserving Machine Learning against Malicious Adversaries
Tianpei Lu, Bingsheng Zhang, Lichun Li +1
With the increasing emphasis on privacy regulations, such as GDPR, protecting individual privacy and ensuring compliance have become critical concerns for both individuals and orga…