3 citations · 4 across the 6 of their papers we have counts for
6 papers
HeteroFedSyn: Differentially Private Tabular Data Synthesis for Heterogeneous Federated Settings
Xiaochen Li, Fengyu Gao, Xizixiang Wei +3
Traditional Differential Privacy (DP) mechanisms are typically tailored to specific analysis tasks, which limits the reusability of protected data. DP tabular data synthesis overco…
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning
Yujia Mu, Xizixiang Wei, Cong Shen
Wireless federated learning (FL) relies on efficient uplink communications to aggregate model updates across distributed edge devices. Over-the-air computation (a.k.a. AirComp) has…
Differentially Private Wireless Federated Learning Using Orthogonal Sequences
Xizixiang Wei, Tianhao Wang, Ruiquan Huang +3
We propose a privacy-preserving uplink over-the-air computation (AirComp) method, termed FLORAS, for single-input single-output (SISO) wireless federated learning (FL) systems. Fro…
Random Orthogonalization for Federated Learning in Massive MIMO Systems
Xizixiang Wei, Cong Shen, Jing Yang +1
We propose a novel communication design, termed random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. The ke…
Random Orthogonalization for Federated Learning in Massive MIMO Systems
Xizixiang Wei, Cong Shen, Jing Yang +1
We propose a novel uplink communication method, coined random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system.…
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples
Xizixiang Wei, Cong Shen
Does Federated Learning (FL) work when both uplink and downlink communications have errors? How much communication noise can FL handle and what is its impact to the learning perfor…