Optimal MIMO Combining for Blind Federated Edge Learning with Gradient Sparsification

03/24/2022
by   Ema Becirovic, et al.
0

We provide the optimal receive combining strategy for federated learning in multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform individual gradient sparsification which greatly improves performance in scenarios with heterogeneous (non i.i.d.) training data. The proposed method beats the benchmark by a wide margin.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/12/2022

Communication-Efficient Federated Learning over MIMO Multiple Access Channels

Communication efficiency is of importance for wireless federated learnin...
research
06/03/2023

Over-the-Air Federated Learning In Broadband Communication

Federated learning (FL) is a privacy-preserving distributed machine lear...
research
03/18/2020

Gradient Estimation for Federated Learning over Massive MIMO Communication Systems

Federated learning is a communication-efficient and privacy-preserving s...
research
01/31/2023

Federated Cell-Free MIMO in Non-Terrestrial Networks: Architectures and Performance

While 5G networks are being rolled out, the definition of the potential ...
research
01/29/2022

Random Orthogonalization for Federated Learning in Massive MIMO Systems

We propose a novel uplink communication method, coined random orthogonal...
research
12/17/2021

Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach

We present a federated learning framework that is designed to robustly d...
research
09/15/2021

Federated Learning of Molecular Properties in a Heterogeneous Setting

Chemistry research has both high material and computational costs to con...

Please sign up or login with your details

Forgot password? Click here to reset