most citedCSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

cs.DC2023★ 1 cited

CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning

Xiang Ma, Qun Wang, Haijian Sun +2

Asynchronous federated learning aims to solve the straggler problem in heterogeneous environments, i.e., clients have small computational capacities that could cause aggregation de…

cs.DC2023

Approximate Wireless Communication for Federated Learning

Xiang Ma, Haijian Sun, Rose Qingyang Hu +1

This paper presents an approximate wireless communication scheme for federated learning (FL) model aggregation in the uplink transmission. We consider a realistic channel that reve…

cs.CR2022

A New Implementation of Federated Learning for Privacy and Security Enhancement

Xiang Ma, Haijian Sun, Rose Qingyang Hu +1

Motivated by the ever-increasing concerns on personal data privacy and the rapidly growing data volume at local clients, federated learning (FL) has emerged as a new machine learni…