FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity-Reference-Cited by-同舟云学术

FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity

Published:2022-08-29 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 51st International Conference on Parallel Processing
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Author:

Li Guanghao¹,Hu Yue¹,Zhang Miao¹,Liu Ji²,Yin Quanjun¹,Peng Yong¹,Dou Dejing²

Affiliation:

1. College of Systems Engineering, National University of Defense Technology, China

2. Baidu research, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3545008.3545065

Reference34 articles.

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3. FedAT

4. Zheng Chai , Hannan Fayyaz , Zeshan Fayyaz , Ali Anwar , Yi Zhou , Nathalie Baracaldo , Heiko Ludwig , and Yue Cheng . 2019 . Towards taming the resource and data heterogeneity in federated learning . In USENIX Conference on Operational Machine Learning (OpML 19) . 19–21. Zheng Chai, Hannan Fayyaz, Zeshan Fayyaz, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig, and Yue Cheng. 2019. Towards taming the resource and data heterogeneity in federated learning. In USENIX Conference on Operational Machine Learning (OpML 19). 19–21.

5. Asynchronous Online Federated Learning for Edge Devices with Non-IID Data

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