26 citations · 38 across the 5 of their papers we have counts for
5 papers
Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation
Yiwei Li, Shuai Wang, Zhuojun Tian +2
Federated Learning (FL) often adopts differential privacy (DP) to protect client data, but the added noise required for privacy guarantees can substantially degrade model accuracy.…
Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity
Zhuojun Tian, Zhaoyang Zhang, Yiwei Li +1
To jointly tackle the challenges of data and node heterogeneity in decentralized learning, we propose a distributed strong lottery ticket hypothesis (DSLTH), based on which a commu…
Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification
Yiwei Li, Chien-Wei Huang, Shuai Wang +2
Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter…
Differentially Private Federated Clustering over Non-IID Data
Yiwei Li, Shuai Wang, Chong-Yung Chi +1
In this paper, we investigate federated clustering (FedC) problem, that aims to accurately partition unlabeled data samples distributed over massive clients into finite clusters un…
Federated Stochastic Primal-dual Learning with Differential Privacy
Yiwei Li, Shuai Wang, Tsung-Hui Chang +1
Federated learning (FL) is a new paradigm that enables many clients to jointly train a machine learning (ML) model under the orchestration of a parameter server while keeping the l…