Communication efficiency optimization of federated learning for computing and network convergence of 6G networks

Yizhuo CAI; Bo LEI; Qianying ZHAO; Jing PENG; Min WEI; Yushun ZHANG; Xing ZHANG

doi:10.1631/FITEE.2300122

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Communication efficiency optimization of federated learning for computing and network convergence of 6G networks

Special Feature on Coordination of Networking and Computing: Architecture, Theory, and Practice | Updated：2024-06-03

- Communication efficiency optimization of federated learning for computing and network convergence of 6G networks
- 面向6G算力网络的联邦学习通信效率优化
- “In the realm of 6G networks, the research on federated learning has made significant strides. The computing and network convergence (CNC) paradigm, which is integral to 6G, offers a new architecture that enhances federated learning's training and communication efficiency. By leveraging the CNC's capabilities to guide device training in federated learning, the research optimizes communication efficiency in complex networks, effectively balancing device delay and improving resource utilization.”
- Frontiers of Information Technology & Electronic Engineering Vol. 25, Issue 5, Pages: 713-727(2024)
- Affiliations：
  
  1.Wireless Signal Processing and Network Laboratory, Beijing University of Posts and Telecommunications, Beijing 100876, China
  2.Research Institute of China Telecom Co., Ltd., Beijing 102209, China
  3.Beijing Telecom, Beijing 100011, China
- Author bio：
  
  ‡Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2300122
  CLC： TP393
- Received：28 February 2023，
  
  Accepted：17 October 2023，
  
  Published：0 May 2024
- Accepted：
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Yizhuo CAI, Bo LEI, Qianying ZHAO, et al. Communication efficiency optimization of federated learning for computing and network convergence of 6G networks[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(5): 713-727.
DOI：

Yizhuo CAI, Bo LEI, Qianying ZHAO, et al. Communication efficiency optimization of federated learning for computing and network convergence of 6G networks[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(5): 713-727. DOI： 10.1631/FITEE.2300122.

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Communication efficiency optimization of federated learning for computing and network convergence of 6G networks

DOI：10.1631/FITEE.2300122