7 citations · 19 across the 7 of their papers we have counts for
7 papers
Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning
Jieming Bian, Cong Shen, Jie Xu
Federated Learning (FL) is a machine learning approach that enables the creation of shared models for powerful applications while allowing data to remain on devices. This approach…
On the Local Cache Update Rules in Streaming Federated Learning
Heqiang Wang, Jieming Bian, Jie Xu
In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capac…
Federated Learning via Indirect Server-Client Communications
Jieming Bian, Cong Shen, Jie Xu
Federated Learning (FL) is a communication-efficient and privacy-preserving distributed machine learning framework that has gained a significant amount of research attention recent…
On the Convergence of Multi-Server Federated Learning with Overlapping Area
Zhe Qu, Xingyu Li, Jie Xu +3
Multi-server Federated learning (FL) has been considered as a promising solution to address the limited communication resource problem of single-server FL. We consider a typical mu…
An Overview on Over-the-Air Federated Edge Learning
Xiaowen Cao, Zhonghao Lyu, Guangxu Zhu +3
Over-the-air federated edge learning (Air-FEEL) has emerged as a promising solution to support edge artificial intelligence (AI) in future beyond 5G (B5G) and 6G networks. In Air-F…
Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying
Jieming Bian, Jie Xu
This paper presents a study on asynchronous Federated Learning (FL) in a mobile network setting. The majority of FL algorithms assume that communication between clients and the ser…
Combating Client Dropout in Federated Learning via Friend Model Substitution
Heqiang Wang, Jie Xu
Federated learning (FL) is a new distributed machine learning framework known for its benefits on data privacy and communication efficiency. Since full client participation in many…