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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2023

11.11.2022 | Original Article

A survey on federated learning: challenges and applications

verfasst von: Jie Wen, Zhixia Zhang, Yang Lan, Zhihua Cui, Jianghui Cai, Wensheng Zhang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2023

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Abstract

Federated learning (FL) is a secure distributed machine learning paradigm that addresses the issue of data silos in building a joint model. Its unique distributed training mode and the advantages of security aggregation mechanism are very suitable for various practical applications with strict privacy requirements. However, with the deployment of FL mode into practical application, some bottlenecks appear in the FL training process, which affects the performance and efficiency of the FL model in practical applications. Therefore, more researchers have paid attention to the challenges of FL and sought for various effective research methods to solve these current bottlenecks. And various research achievements of FL have been made to promote the intelligent development of all application areas with privacy restriction. This paper systematically introduces the current researches in FL from five aspects: the basics knowledge of FL, privacy and security protection mechanisms in FL, communication overhead challenges and heterogeneity problems of FL. Furthermore, we make a comprehensive summary of the research in practical applications and prospect the future research directions of FL.
Vorheriger Artikel Cross-domain pattern classification with heterogeneous distribution adaptation
Nächster Artikel Attention-based adaptive context network for anchor-free instance segmentation

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Metadaten
Titel
A survey on federated learning: challenges and applications
verfasst von
Jie Wen
Zhixia Zhang
Yang Lan
Zhihua Cui
Jianghui Cai
Wensheng Zhang
Publikationsdatum
11.11.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2023
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-022-01647-y

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