EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence

Author:

Dong Zeqian1ORCID,He Qiang2ORCID,Chen Feifei3ORCID,Jin Hai4ORCID,Gu Tao5ORCID,Yang Yun1ORCID

Affiliation:

1. Swinburne University of Technology, Australia

2. Huazhong University of Science and Technology, China and Swinburne University of Technology, Australia

3. Deakin University, Australia

4. Huazhong University of Science and Technology, China

5. Macquarie University, Australia

Funder

Australian Research Council

Publisher

ACM

Reference93 articles.

1. Meta AI. 2021. PyTorch Mobile. https://pytorch.org/mobile Meta AI. 2021. PyTorch Mobile. https://pytorch.org/mobile

2. Romil Bhardwaj , Zhengxu Xia , Ganesh Ananthanarayanan , Junchen Jiang , Yuanchao Shu , Nikolaos Karianakis , Kevin Hsieh , Paramvir Bahl , and Ion Stoica . 2022 . Ekya: Continuous learning of video analytics models on edge compute servers . In 19th USENIX Symposium on Networked Systems Design and Implementation. 119–135 . Romil Bhardwaj, Zhengxu Xia, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Nikolaos Karianakis, Kevin Hsieh, Paramvir Bahl, and Ion Stoica. 2022. Ekya: Continuous learning of video analytics models on edge compute servers. In 19th USENIX Symposium on Networked Systems Design and Implementation. 119–135.

3. Distributed distillation for on-device learning;Bistritz Ilai;Advances in Neural Information Processing Systems,2020

4. Towards federated learning at scale: System design;Bonawitz Keith;Proceedings of Machine Learning and Systems,2019

5. Large-Scale Machine Learning with Stochastic Gradient Descent

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