123 citations · 130 across the 7 of their papers we have counts for
8 papers
Ask Without Telling: Local SLMs Consult Cloud LLMs Without Revealing Task Intent
Yanmeng Wang, Yunxuan Li, Shilong Fan +2
As local small language models (SLMs) increasingly collaborate with more capable cloud large language models (LLMs), a natural privacy question arises: Can a local SLM obtain cloud…
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples
Xiaotong Zhao, Mian Li, Bo Wang +3
Recently, the decentralized baseband processing (DBP) paradigm and relevant detection methods have been proposed to enable extremely large-scale massive multiple-input multiple-out…
-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning
Zhiwei Tang, Yanmeng Wang, Tsung-Hui Chang
Federated Learning (FL) is a promising privacy-preserving distributed learning paradigm but suffers from high communication cost when training large-scale machine learning models.…
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…
Why Batch Normalization Damage Federated Learning on Non-IID Data?
Yanmeng Wang, Qingjiang Shi, Tsung-Hui Chang
As a promising distributed learning paradigm, federated learning (FL) involves training deep neural network (DNN) models at the network edge while protecting the privacy of the edg…
Low-rank Matrix Recovery With Unknown Correspondence
Zhiwei Tang, Tsung-Hui Chang, Xiaojing Ye +1
We study a matrix recovery problem with unknown correspondence: given the observation matrix , where is an unknown permutation matrix, we aim to reco…
A Dimension Reduction Technique for Large-scale Structured Sparse Optimization Problems with Application to Convex Clustering
Yancheng Yuan, Tsung-Hui Chang, Defeng Sun +1
In this paper, we propose a novel adaptive sieving (AS) technique and an enhanced AS (EAS) technique, which are solver independent and could accelerate optimization algorithms for…
Quantized Federated Learning under Transmission Delay and Outage Constraints
Yanmeng Wang, Yanqing Xu, Qingjiang Shi +1
Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wirel…