3 citations · 11 across the 12 of their papers we have counts for
6 papers · 1 filter
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
Ji Liu, Beichen Ma, Qiaolin Yu +7
Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices. The data distributed…
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…
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices
Ji Liu, Tianshi Che, Yang Zhou +4
Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exc…
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…
HexGen: Generative Inference of Large Language Model over Heterogeneous Environment
Youhe Jiang, Ran Yan, Xiaozhe Yao +3
Serving generative inference of the large language model is a crucial component of contemporary AI applications. This paper focuses on deploying such services in a heterogeneous an…