Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks-Reference-Cited by-同舟云学术

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Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks

Published:2022-05-02 Issue: Volume: Page:
ISSN:
Container-title:IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
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Author:

Baek Hankyul¹,Yun Won Joon¹,Kwak Yunseok¹,Jung Soyi²,Ji Mingyue³,Bennis Mehdi⁴,Park Jihong⁵,Kim Joongheon¹

Affiliation:

1. Korea University,Department of Electrical and Computer Engineering,Seoul,Republic of Korea

2. Hallym University,School of Software,Chuncheon,Republic of Korea

3. University of Utah,Department of Electrical and Computer Engineering,Salt Lake City,UT,USA

4. University of Oulu,Centre for Wireless Communications,Oulu,Finland

5. Deakin University,School of Information Technology,Geelong,Australia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9796607/9796652/09796733.pdf?arnumber=9796733

Reference23 articles.

1. Depth-Controllable Very Deep Super-Resolution Network

2. Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing

3. Fundamentals of Wireless Communication

4. Application of Non-Orthogonal Multiple Access in LTE and 5G Networks

5. A field guide to federated optimization;wang,2021

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1. Federated Edge Learning with Blurred or Pseudo Data Sharing;Proceedings of the 53rd International Conference on Parallel Processing;2024-08-12

2. Federated Learning With Non-IID Data: A Survey;IEEE Internet of Things Journal;2024-06-01

3. Dynamic Quantum Federated Learning for Satellite-Ground Integrated Systems Using Slimmable Quantum Neural Networks;IEEE Access;2024

4. SlimFL: Federated Learning With Superposition Coding Over Slimmable Neural Networks;IEEE/ACM Transactions on Networking;2023-12

5. AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices;IEEE INFOCOM 2023 - IEEE Conference on Computer Communications;2023-05-17

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