2 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.CR2025
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…