The cost of training machine learning models over distributed data sources
Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data. However, it requires a central server to coordinate the learning process, thus introdu...
| Autores: | , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
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| Acesso em linha: | https://hdl.handle.net/10230/73074 http://dx.doi.org/10.1109/OJCOMS.2023.3274394 |
| Access Level: | acceso abierto |
| Palavra-chave: | Blockchain Decentralized learning Edge computingk Energy efficiency Federated learning Machine learning |
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The cost of training machine learning models over distributed data sourcesGuerra, EliaWilhelmi Roca, FrancescMiozzo, MarcoDini, PaoloBlockchainDecentralized learningEdge computingkEnergy efficiencyFederated learningMachine learningFederated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data. However, it requires a central server to coordinate the learning process, thus introducing potential scalability and security issues. In the literature, server-less federated learning approaches like gossip federated learning and blockchain-enabled federated learning have been proposed to mitigate these issues. In this work, we propose a complete overview of these three techniques, proposing a comparison according to an integral set of performance indicators, including model accuracy, time complexity, communication overhead, convergence time, and energy consumption. An extensive simulation campaign permits to draw a quantitative analysis considering both feedforward and convolutional neural network models. Results show that gossip federated learning and standard federated solution are able to reach a similar level of accuracy, and their energy consumption is influenced by the machine learning model adopted, the software library, and the hardware used. Differently, blockchain-enabled federated learning represents a viable solution for implementing decentralized learning with a higher level of security, at the cost of an extra energy usage and data sharing. Finally, we identify open issues on the two decentralized federated learning implementations and provide insights on potential extensions and possible research directions on this new research field.This work was supported in part by the Spanish Project PID2020-113832RB-C22 (ORIGIN)/MCIN/AEI/10.13039/50110001103; in part by the European Union Horizon 2020 Research and Innovation Programme under Grant 953775 (GREENEDGE); and in part by CHIST-ERA-20-SICT-004 (SONATA) under Grant PCI2021-122043-2A/AEI/10.13039/501100011033.Institute of Electrical and Electronics Engineers (IEEE)2026202620232026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/10230/73074http://dx.doi.org/10.1109/OJCOMS.2023.3274394https://hdl.handle.net/10230/73074reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésIEEE Open Journal of the Communications Society. 2023;4:1111-26info:eu-repo/grantAgreement/ES/2PE/PID2020-113832RB-C22info:eu-repo/grantAgreement/EC/H2020/953775info:eu-repo/grantAgreement/ES/2PE/PCI2021-122043-2ACCBY - IEEE is not the copyright holder of this material. Please follow the instructions via https://creativecommons.org/licenses/by/4.0/ to obtain full-text articles and stipulations in the API documentation.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:recercat____::902691ad2f5720ab584811d983a0d6b82026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
The cost of training machine learning models over distributed data sources |
| title |
The cost of training machine learning models over distributed data sources |
| spellingShingle |
The cost of training machine learning models over distributed data sources Guerra, Elia Blockchain Decentralized learning Edge computingk Energy efficiency Federated learning Machine learning |
| title_short |
The cost of training machine learning models over distributed data sources |
| title_full |
The cost of training machine learning models over distributed data sources |
| title_fullStr |
The cost of training machine learning models over distributed data sources |
| title_full_unstemmed |
The cost of training machine learning models over distributed data sources |
| title_sort |
The cost of training machine learning models over distributed data sources |
| dc.creator.none.fl_str_mv |
Guerra, Elia Wilhelmi Roca, Francesc Miozzo, Marco Dini, Paolo |
| author |
Guerra, Elia |
| author_facet |
Guerra, Elia Wilhelmi Roca, Francesc Miozzo, Marco Dini, Paolo |
| author_role |
author |
| author2 |
Wilhelmi Roca, Francesc Miozzo, Marco Dini, Paolo |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
Blockchain Decentralized learning Edge computingk Energy efficiency Federated learning Machine learning |
| topic |
Blockchain Decentralized learning Edge computingk Energy efficiency Federated learning Machine learning |
| description |
Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data. However, it requires a central server to coordinate the learning process, thus introducing potential scalability and security issues. In the literature, server-less federated learning approaches like gossip federated learning and blockchain-enabled federated learning have been proposed to mitigate these issues. In this work, we propose a complete overview of these three techniques, proposing a comparison according to an integral set of performance indicators, including model accuracy, time complexity, communication overhead, convergence time, and energy consumption. An extensive simulation campaign permits to draw a quantitative analysis considering both feedforward and convolutional neural network models. Results show that gossip federated learning and standard federated solution are able to reach a similar level of accuracy, and their energy consumption is influenced by the machine learning model adopted, the software library, and the hardware used. Differently, blockchain-enabled federated learning represents a viable solution for implementing decentralized learning with a higher level of security, at the cost of an extra energy usage and data sharing. Finally, we identify open issues on the two decentralized federated learning implementations and provide insights on potential extensions and possible research directions on this new research field. |
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2023 |
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2023 2026 2026 2026 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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https://hdl.handle.net/10230/73074 http://dx.doi.org/10.1109/OJCOMS.2023.3274394 https://hdl.handle.net/10230/73074 |
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https://hdl.handle.net/10230/73074 http://dx.doi.org/10.1109/OJCOMS.2023.3274394 |
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Inglés |
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Inglés |
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IEEE Open Journal of the Communications Society. 2023;4:1111-26 info:eu-repo/grantAgreement/ES/2PE/PID2020-113832RB-C22 info:eu-repo/grantAgreement/EC/H2020/953775 info:eu-repo/grantAgreement/ES/2PE/PCI2021-122043-2A |
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Institute of Electrical and Electronics Engineers (IEEE) |
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