7 citations · 25 across the 18 of their papers we have counts for
5 papers · 1 filter
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
Jieming Bian, Lei Wang, Shaolei Ren +1
Training large-scale artificial intelligence (AI) models demands significant computational power and energy, leading to increased carbon footprint with potential environmental repe…
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…
Accelerating Hybrid Federated Learning Convergence under Partial Participation
Jieming Bian, Lei Wang, Kun Yang +2
Over the past few years, Federated Learning (FL) has become a popular distributed machine learning paradigm. FL involves a group of clients with decentralized data who collaborate…
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…