FHDnn-Reference-Cited by-同舟云学术

FHDnn

Published:2022-07-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 59th ACM/IEEE Design Automation Conference
language:
Short-container-title:

Author:

Chandrasekaran Rishikanth¹,Ergun Kazim¹,Lee Jihyun¹,Nanjunda Dhanush¹,Kang Jaeyoung¹,Rosing Tajana¹

Affiliation:

1. UC San Diego

Funder

NSF

CRISP

SRC Global Research Collaboration (GRC)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3489517.3530394

Reference21 articles.

1. [n. d.]. UCI machine learning repository. http://archive.ics.uci.edu/ml/datasets/ISOLET. [n. d.]. UCI machine learning repository. http://archive.ics.uci.edu/ml/datasets/ISOLET.

2. Ganesh Ananthanarayanan et al. 2017. Real-time video analytics: The killer app for edge computing. computer 50 10 (2017) 58--67. Ganesh Ananthanarayanan et al. 2017. Real-time video analytics: The killer app for edge computing. computer 50 10 (2017) 58--67.

3. Austin P Arechiga etal 2018. The robustness of modern deep learning architectures against single event upset errors. In 2018 IEEE High Performance extreme Computing Conference (HPEC). IEEE 1--6. Austin P Arechiga et al. 2018. The robustness of modern deep learning architectures against single event upset errors. In 2018 IEEE High Performance extreme Computing Conference (HPEC). IEEE 1--6.

4. Nader Bouacida et al. 2021. Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning. In INFOCOM WKSHPS. IEEE 1--6. Nader Bouacida et al. 2021. Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning. In INFOCOM WKSHPS. IEEE 1--6.

5. Sebastian Caldas et al. 2019. Expanding the Reach of Federated Learning by Reducing Client Resource Requirements. arXiv:1812.07210 (Jan 2019). Sebastian Caldas et al. 2019. Expanding the Reach of Federated Learning by Reducing Client Resource Requirements. arXiv:1812.07210 (Jan 2019).

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