123 citations · 130 across the 7 of their papers we have counts for
4 papers · 1 filter
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…