3 citations · 11 across the 12 of their papers we have counts for
Showing 2023 · cs.DCShow all
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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.DC2023★ 3 cited
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…