A survey on federated learning

Zhang, Chen; Xie, Yu; Bai, Hang; Yu, Bin; Li, Weihong; Gao, Yimin

doi:10.1016/j.knosys.2021.106775

Cited by 731 publications

(299 citation statements)

References 35 publications

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“…In this tutorial, we briefly discussed the evolving field of federated learning and outlined recent achievements and approaches. A detailed analysis of recent trends and problems in FL is also provided in [51,52,53,54]. Due to additional privacy constraints in ML and a variety of distributed user groups of ML methods, it can be expected that FL will become even more important in the future ESANN 2021 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.…”

Section: Discussionmentioning

confidence: 99%

Federated Learning - Methods, Applications and beyond

Heusinger¹,

Raab²,

Rossi³

et al. 2021

ESANN 2021 Proceedings

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In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress. While some domains like web analysis can benefit from this with only minor restrictions, other fields like medicine with patient data are stronger regulated. In particular data privacy plays an important role as recently highlighted by the trustworthy AI initiative of the EU or general privacy regulations in legislation. Another major challenge is, that the required training data is often distributed in terms of features or samples and unavailable for classical batch learning approaches. In 2016 Google came up with a framework, called Federated Learning to solve both of these problems. We provide a brief overview on existing Methods and Applications in the field of vertical and horizontal Federated Learning, as well as Federated Transfer Learning.

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Section: Discussionmentioning

confidence: 99%

Federated Learning - Methods, Applications and beyond

Heusinger¹,

Raab²,

Rossi³

et al. 2021

ESANN 2021 Proceedings

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“…It appears in machine learning as a promising topic for research. Refer to the subject reviews ( [157], [158]) for interested readers.…”

Section: And Explainability: a Machine Learning Zoo Mini-tour And Explainable Ai A Review Of Machine Learning Interpretability Methods Pamentioning

confidence: 99%

On Deep Research Problems in Deep Learning

Ulhaq¹

2021

Preprint

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The subject of deep learning has emerged in the last decade as one of the most promising approaches to machine learning. Today, certainly, much of the recent progress in artificial intelligence is due to it, but research challenges are still unresolved and remain open to the research community. This paper attempts to offer a comprehensive review of deep learning progress in active research frontiers. On the one side, by presenting a brief overview of deep learning success, we inspire researchers to work in deep learning. On the other hand, we examine a range of technical issues, and open research issues that we believe are relevant topics for exploratory research. As deep learning applies to various fields, we restrict this paper’s scope to visual recognition tasks to analyze these problems with a specific lens. However, these problems will be broadly applicable to other fields. It will make it easier for new researchers to recognize outstanding research problems in the deep learning domain.

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“…Vertical federated learning is the case when features are different and the samples partially overlap. Federated transfer learning is used when neither the features nor samples overlap [45].…”

Section: Distributed Learning Overviewmentioning

confidence: 99%

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Gholizadeh

Musilek

2021

Energies

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In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

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A survey on federated learning

Cited by 731 publications

References 35 publications

Federated Learning - Methods, Applications and beyond

Federated Learning - Methods, Applications and beyond

On Deep Research Problems in Deep Learning

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

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