research-article

Online federated learning with imbalanced class distribution

Authors:

Konstantinos Giorgas,

Iraklis VarlamisAuthors Info & Claims

PCI '20: Proceedings of the 24th Pan-Hellenic Conference on Informatics

Pages 91 - 95

https://doi.org/10.1145/3437120.3437282

Published: 04 March 2021 Publication History

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Abstract

The federated learning paradigm can be a viable solution for handling huge datasets, and for taking advantage of powerful processing nodes on the edge. The process of online federated learning can be employed in order to maximise the potential of federated learning by re-training a shared model on the edge nodes and merging the updated models centrally. This approach allows edge nodes to exchange knowledge without exchanging their own training data, thus preserving their privacy. In this work, we examine the online federated learning approach in an extreme case of imbalanced class distribution between the central and the edge nodes. We examine the effects of different parameters of the online federated learning process and propose a technique that boosts the classification performance above that of the baseline centralised learning approach.

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Cited By

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Chang ZHosseinalipour SChiang MBrinton C(2024)Asynchronous Multi-Model Dynamic Federated Learning Over Wireless Networks: Theory, Modeling, and OptimizationIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2024.339132910:5(1989-2004)Online publication date: Oct-2024
https://doi.org/10.1109/TCCN.2024.3391329
Dai SMeng F(2022)Addressing modern and practical challenges in machine learning: a survey of online federated and transfer learningApplied Intelligence10.1007/s10489-022-04065-353:9(11045-11072)Online publication date: 30-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-022-04065-3

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Published In

PCI '20: Proceedings of the 24th Pan-Hellenic Conference on Informatics

November 2020

433 pages

ISBN:9781450388979

DOI:10.1145/3437120

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 04 March 2021

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PCI 2020

PCI 2020: 24th Pan-Hellenic Conference on Informatics

November 20 - 22, 2020

Athens, Greece

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Chang ZHosseinalipour SChiang MBrinton C(2024)Asynchronous Multi-Model Dynamic Federated Learning Over Wireless Networks: Theory, Modeling, and OptimizationIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2024.339132910:5(1989-2004)Online publication date: Oct-2024
https://doi.org/10.1109/TCCN.2024.3391329
Dai SMeng F(2022)Addressing modern and practical challenges in machine learning: a survey of online federated and transfer learningApplied Intelligence10.1007/s10489-022-04065-353:9(11045-11072)Online publication date: 30-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-022-04065-3

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Triplets Oversampling for Class Imbalanced Federated Datasets

FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification

Multimodal federated learning: Concept, methods, applications and future directions

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