7 citations · 8 across the 4 of their papers we have counts for
5 papers
Robust Federated Fine-Tuning in Heterogeneous Networks with Unreliable Connections: An Aggregation View
Yanmeng Wang, Zhiwen Dai, Shuai Wang +4
Federated Fine-Tuning (FFT) has attracted growing interest as it leverages both server- and client-side data to enhance global model generalization while preserving privacy, and si…
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework
Shuai Wang, Yanqing Xu, Zhiguo Wang +3
As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and…
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…
Federated Matrix Factorization: Algorithm Design and Application to Data Clustering
Shuai Wang, Tsung-Hui Chang
Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks. Although many FL algorithms hav…
Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization
Shuai Wang, Tsung-Hui Chang, Ying Cui +1
The non-negative matrix factorization (NMF) model with an additional orthogonality constraint on one of the factor matrices, called the orthogonal NMF (ONMF), has been found a prom…