16 citations · 18 across the 8 of their papers we have counts for
4 papers · 1 filter
Efficient Federated Learning with Timely Update Dissemination
Juncheng Jia, Ji Liu, Chao Huo +4
Federated Learning (FL) has emerged as a compelling methodology for the management of distributed data, marked by significant advancements in recent years. In this paper, we propos…
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
Ji Liu, Juncheng Jia, Hong Zhang +5
Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this pape…
Efficient Asynchronous Federated Learning with Sparsification and Quantization
Juncheng Jia, Ji Liu, Chendi Zhou +3
While data is distributed in multiple edge devices, Federated Learning (FL) is attracting more and more attention to collaboratively train a machine learning model without transfer…
FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-aware Model Update
Ji Liu, Juncheng Jia, Tianshi Che +5
As a promising approach to deal with distributed data, Federated Learning (FL) achieves major advancements in recent years. FL enables collaborative model training by exploiting th…