AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices-Reference-Cited by-同舟云学术

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AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

Published:2023-05-17 Issue: Volume: Page:
ISSN:
Container-title:IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
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Author:

Li Peichun¹,Cheng Guoliang¹,Huang Xumin¹,Kang Jiawen¹,Yu Rong¹,Wu Yuan²,Pan Miao³

Affiliation:

1. Guangdong University of Technology,School of Automation,Guangzhou,China

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

3. University of Houston,Department of Electrical and Computer Engineering,Houston,USA

Funder

National Natural Science Foundation of China

Technology Development

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10228851/10228852/10229017.pdf?arnumber=10229017

Reference45 articles.

1. FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing

2. Federated learning: Strategies for improving communication efficiency;kone?n?;arXiv preprint arXiv 1610 05492,2016

3. To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices

4. Federated learning with matched averaging;wang;ICLRE,2020

5. Optimal Rate Adaption in Federated Learning with Compressed Communications

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2. Filling the Missing: Exploring Generative AI for Enhanced Federated Learning Over Heterogeneous Mobile Edge Devices;IEEE Transactions on Mobile Computing;2024-10

3. FedGreen: Carbon-Aware Federated Learning with Model Size Adaptation;2024 IEEE International Conference on Communications Workshops (ICC Workshops);2024-06-09

4. GraphProxy: Communication-Efficient Federated Graph Learning with Adaptive Proxy;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20

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