A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends-Reference-Cited by-同舟云学术

A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends

Published:2024-04 Issue:4 Volume:11 Page:824-850
ISSN:2329-9266
Container-title:IEEE/CAA Journal of Automatica Sinica
language:
Short-container-title:IEEE/CAA J. Autom. Sinica

Author:

Luzón M. Victoria¹^ORCID,Rodríguez-Barroso Nuria²^ORCID,Argente-Garrido Alberto²^ORCID,Jiménez-López Daniel²^ORCID,Moyano Jose M.²^ORCID,Del Ser Javier³^ORCID,Ding Weiping⁴^ORCID,Herrera Francisco²^ORCID

Affiliation:

1. Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada,Department of Software Engineering,Granada,Spain,18071

2. Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada,Department of Computer Science and Artificial Intelligence,Granada,Spain,18071

3. University of the Basque Country (UPV/EHU),Department of Communications Engineering,Spain

4. School of Information Science and Technology, Nantong University,Nantong,China,226019

Funder

European Union

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6570654/10475140/10475194.pdf?arnumber=10475194

Reference129 articles.

1. Deep learning

2. Challenges for machine learning on distributed platforms (invited talk);Goldstein

3. Federated learning: Strategies for improving communication efficiency;Konečnỳ;arXiv preprint,2016

4. Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions

Cited by 3 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. Blockchain and Trustworthy Reputation for Federated Learning: Opportunities and Challenges;2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom);2024-07-08

2. Defense Strategy against Byzantine Attacks in Federated Machine Learning: Developments towards Explainability;2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE);2024-06-30

3. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024