Heterogeneous Edge Processing
Heterogeneous Edge Processing is a computational framework that orchestrates data processing tasks across diverse edge devices with varying hardware capabilities, software environments, and network conditions to enable efficient and adaptive decentralized computation.
8
Publications
266
Citations
46
Authors
23
Institutions
Publications per year
2017–2026
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Parents
8
Adaptive parallel execution of deep neural networks on heterogeneous edge devices
Li Zhou, Mohammad Hossein Samavatian, Anys Bacha et al. · 2019 · 103 citations · Full text
Customized Federated Learning for accelerated edge computing with heterogeneous task targets
Hui Jiang, Min Liu, Bo Yang et al. · Computer Networks · 2020 · 39 citations
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23
Virginia Tech
Blacksburg, United States
1 h-index
1 publications
23 citations

Chinese Academy of Sciences
Beijing, China
1 h-index
4 publications
156 citations
PLA Army Engineering University
Nanjing, China
1 h-index
1 publications
18 citations
Centre for Social Innovation
Toronto, Canada
1 h-index
3 publications
54 citations
Lenovo (China)
Beijing, China
1 h-index
1 publications
39 citations
The University of Texas at San Antonio
San Antonio, United States
1 h-index
1 publications
29 citations

University of Toronto
Toronto, Canada
1 h-index
2 publications
36 citations
Institute of Computing Technology
Beijing, China
1 h-index
4 publications
156 citations
1–8 of 23
1 publications · 16 citations
1
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1 publications · 29 citations
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TMCCI · 1 publications · 18 citations
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1 publications · 23 citations
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TPDS · 1 publications · 20 citations
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