1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning
Chih Wei Ling, Chun Hei Michael Shiu, Youqi Wu +4
Training machine learning models on decentralized private data via federated learning (FL) poses two key challenges: communication efficiency and privacy protection. In this work,…
Rejection-Sampled Universal Quantization for Smaller Quantization Errors
Chih Wei Ling, Cheuk Ting Li
We construct a randomized vector quantizer which has a smaller maximum error compared to all known lattice quantizers with the same entropy for dimensions 5, 6, ..., 48, and also h…
Communication-Efficient Laplace Mechanism for Differential Privacy via Random Quantization
Ali Moradi Shahmiri, Chih Wei Ling, Cheuk Ting Li
We propose the first method that realizes the Laplace mechanism exactly (i.e., a Laplace noise is added to the data) that requires only a finite amount of communication (whereas th…