4 papers
Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication
Yilin Xu, Chun Hei Michael Shiu, Chih Wei Ling +1
Record-level differential privacy exposes a structural misalignment in personalized federated learning when client-specific variation is low-dimensional while training repeatedly r…
Communication-Efficient and Privacy-Adaptable Mechanism -- a Federated Learning Scheme with Convergence Analysis
Chun Hei Michael Shiu, Chih Wei Ling
Federated learning enables multiple parties to jointly train learning models without sharing their own underlying data, offering a practical pathway to privacy-preserving collabora…
Information-Theoretic Limits on Exact Subgraph Alignment Problem
Chun Hei Michael Shiu, Hei Victor Cheng, Lele Wang
The graph alignment problem aims to identify the vertex correspondence between two correlated graphs. Most existing studies focus on the scenario in which the two graphs share the…
On the Generalization Error of Differentially Private Algorithms via Typicality
Yanxiao Liu, Chun Hei Michael Shiu, Lele Wang +1
We study the generalization error of stochastic learning algorithms from an information-theoretic perspective, with a particular emphasis on deriving sharper bounds for differentia…