FedHybrid: Hierarchical Hybrid Training for High-Performance Federated Learning-Reference-Cited by-同舟云学术

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FedHybrid: Hierarchical Hybrid Training for High-Performance Federated Learning

Published:2023-09-11 Issue: Volume: Page:
ISSN:
Container-title:2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
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Author:

Chang Tao¹,Li Li²,Wu Meihan¹,Yu Wei³,Wang Xiaodong¹

Affiliation:

1. National University of Defense Technology,National Key Laboratory of Parallel and Distributed Computing,China

2. University of Macau,State Key Laboratory of Internet of Things for Smart City,China

3. The 30th Research Institute of China Electronics Technology Group Corporation,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10287388/10287413/10287503.pdf?arnumber=10287503

Reference27 articles.

1. Gradient-based learning applied to document recognition

2. Towards federated learning at scale: System design;bonawitz;MLSys,2019

3. Very deep convolutional networks for large-scale image recognition;simonyan;ICLRE,2015

4. EMNIST: an extension of MNIST to handwritten letters;cohen;CoRR,2017

5. Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions

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