3 citations · 11 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
Tianshi Che, Ji Liu, Yang Zhou +5
Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. However, the training process of Large Language Models (LLMs) genera…