Federated Learning and Meta Learning: Approaches, Applications, and Directions-Reference-Cited by-同舟云学术

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Federated Learning and Meta Learning: Approaches, Applications, and Directions

Published:2023 Issue: Volume: Page:1-1
ISSN:1553-877X
Container-title:IEEE Communications Surveys & Tutorials
language:
Short-container-title:IEEE Commun. Surv. Tutorials

Author:

Liu Xiaonan¹^ORCID,Deng Yansha²^ORCID,Nallanathan Arumugam³^ORCID,Bennis Mehdi⁴^ORCID

Affiliation:

1. Department of Engineering, King’s College London, U.K.

2. Department of Engineering, King’s College London, London, U.K.

3. School of Electronic Engineering and Computer Science, Queen Mary University of London (QMUL), U.K.

4. Faculty of Information Technology and Electrical Engineering, Centre for Wireless communications, University of Oulu, Finland

Funder

Engineering and Physical Sciences Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/9739/5451756/10310213.pdf?arnumber=10310213

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Federated-Learning Algorithm Based on Client Sampling and Gradient Projection for the Smart Grid;Electronics;2024-05-22

2. Efficient Cross-Project Software Defect Prediction Based on Federated Meta-Learning;Electronics;2024-03-18

3. Editorial First Quarter 2024 IEEE Communications Surveys and Tutorials;IEEE Communications Surveys & Tutorials;2024

4. Federated Meta-Learning: Methodologies and Directions;Lecture Notes in Computer Science;2024

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