1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
VIRGOS: Secure Graph Convolutional Network on Vertically Split Data from Sparse Matrix Decomposition
Yu Zheng, Qizhi Zhang, Lichun Li +2
Securely computing graph convolutional networks (GCNs) is critical for applying their analytical capabilities to privacy-sensitive data like social/credit networks. Multiplying a s…
Exploring Structural Sparsity in Neural Image Compression
Shanzhi Yin, Chao Li, Wen Tan +3
Neural image compression have reached or out-performed traditional methods (such as JPEG, BPG, WebP). However,their sophisticated network structures with cascaded convolution layer…
Universal Efficient Variable-rate Neural Image Compression
Shanzhi Yin, Chao Li, Youneng Bao +1
Recently, Learning-based image compression has reached comparable performance with traditional image codecs(such as JPEG, BPG, WebP). However, computational complexity and rate fle…
Morse-STF: Improved Protocols for Privacy-Preserving Machine Learning
Qizhi Zhang, Sijun Tan, Lichun Li +3
Secure multi-party computation enables multiple mutually distrusting parties to perform computations on data without revealing the data itself, and has become one of the core techn…
MPC Protocol for G-module and its Application in Secure Compare and ReLU
Qizhi Zhang, Lichun Li, Shan Yin +1
Secure comparison and secure selection are two fundamental MPC (secure Multi-Party Computation) protocols. One important application of these protocols is the secure ReLU and DReLU…