An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning

no code implementations • 15 Apr 2024 • Yujia Mu, Xizixiang Wei, Cong Shen

In this paper, we propose an end-to-end communication system supporting AirComp with digital modulation, aiming to overcome the challenges associated with accurate decoding of the sum signal with constellation designs.

Federated Learning

Differentially Private Wireless Federated Learning Using Orthogonal Sequences

no code implementations • 14 Jun 2023 • Xizixiang Wei, Tianhao Wang, Ruiquan Huang, Cong Shen, Jing Yang, H. Vincent Poor

A new FL convergence bound is derived which, combined with the privacy guarantees, allows for a smooth tradeoff between the achieved convergence rate and differential privacy levels.

Federated Learning Privacy Preserving

Random Orthogonalization for Federated Learning in Massive MIMO Systems

no code implementations • 18 Oct 2022 • Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor

We propose a novel communication design, termed random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system.

Federated Learning

Federated Learning over Noisy Channels: Convergence Analysis and Design Examples

no code implementations • 6 Jan 2021 • Xizixiang Wei, Cong Shen

Does Federated Learning (FL) work when both uplink and downlink communications have errors?

Federated Learning