Split-FL: An Efficient Online Federated Learning Framework with Constrained Computation and Streaming Data-Reference-Cited by-同舟云学术

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Split-FL: An Efficient Online Federated Learning Framework with Constrained Computation and Streaming Data

Published:2024-06-09 Issue: Volume:2 Page:661-666
ISSN:
Container-title:2024 IEEE International Conference on Communications Workshops (ICC Workshops)
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Author:

Liu Xiao¹,Xu Lianming²,Wu Xin¹,Zhang Songyang³,Wang Li¹

Affiliation:

1. School of Computer Science, Beijing University of Posts and Telecommunications,Beijing,China

2. School of Electronic Engineering, Beijing University of Posts and Telecommunications,Beijing,China

3. University of Louisiana at Lafayette,Department of Electrical and Computer Engineering,Lafayette,USA

Funder

National Natural Science Foundation of China

Natural Science Foundation of Beijing Municipality

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10615255/10615259/10615347.pdf?arnumber=10615347

Reference17 articles.

1. SpectralGPT: Spectral Remote Sensing Foundation Model

2. When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning

3. Federated optimization in heterogeneous networks;Li,2020

4. Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges

5. Communication-Efficient Federated Learning With Compensated Overlap-FedAvg

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