FLOW: A Scalable Multi-Model Federated Learning Framework on the Wheels-Reference-Cited by-同舟云学术

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FLOW: A Scalable Multi-Model Federated Learning Framework on the Wheels

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)
language:
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Author:

Yao Yongtao¹,Ammar Nejib²,Shi Weisong¹

Affiliation:

1. University of Delaware,Dept. of Computer & Information Sciences,Newark,DE 19716,USA

2. Toyota Motor North America,InfoTech Labs,Mountain View,California,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10210727/10210735/10210780.pdf?arnumber=10210780

Reference54 articles.

1. LEAF: A benchmark for federated settings;caldas,2018

2. Proxylessnas: Direct neural architecture search on target task and hardware;cai,2018

3. Bandit-based communication-efficient client selection strategies for federated learning;cho;2020 54th Asilomar Conference on Signals Systems and Computers,2020

4. Optimal client sampling for federated learning;chen,2020

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