3 citations · 7 across the 6 of their papers we have counts for
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cs.DC2023★ 1 cited
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…
cs.DC2023★ 3 cited
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…
cs.LG2023★ 3 cited
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…