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SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions

As a newly emerging distributed machine learning technology, federated learning has unique advantages in the era of big data. We explore how to motivate participants to experience auctions more actively and safely. It is also essential to ensure that the final participant who wins the right to parti...

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Detalles Bibliográficos
Autores principales: Chen, Qian, Yao, Lin, Wang, Xuan, Lin Jiang, Zoe, Wu, Yulin, Ma, Tianzi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737689/
https://www.ncbi.nlm.nih.gov/pubmed/36502140
http://dx.doi.org/10.3390/s22239434
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author Chen, Qian
Yao, Lin
Wang, Xuan
Lin Jiang, Zoe
Wu, Yulin
Ma, Tianzi
author_facet Chen, Qian
Yao, Lin
Wang, Xuan
Lin Jiang, Zoe
Wu, Yulin
Ma, Tianzi
author_sort Chen, Qian
collection PubMed
description As a newly emerging distributed machine learning technology, federated learning has unique advantages in the era of big data. We explore how to motivate participants to experience auctions more actively and safely. It is also essential to ensure that the final participant who wins the right to participate can guarantee relatively high−quality data or computational performance. Therefore, a secure, necessary and effective mechanism is needed through strict theoretical proof and experimental verification. The traditional auction theory is mainly oriented to price, not giving quality issues as much consideration. Hence, it is challenging to discover the optimal mechanism and solve the privacy problem when considering multi−dimensional auctions. Therefore, we (1) propose a multi−dimensional information security mechanism, (2) propose an optimal mechanism that satisfies the Pareto optimality and incentive compatibility named the SecMDGM and (3) verify that for the aggregation model based on vertical data, this mechanism can improve the performance by 2.73 times compared to that of random selection. These are all important, and they complement each other instead of being independent or in tandem. Due to security issues, it can be ensured that the optimal multi−dimensional auction has practical significance and can be used in verification experiments.
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spelling pubmed-97376892022-12-11 SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions Chen, Qian Yao, Lin Wang, Xuan Lin Jiang, Zoe Wu, Yulin Ma, Tianzi Sensors (Basel) Article As a newly emerging distributed machine learning technology, federated learning has unique advantages in the era of big data. We explore how to motivate participants to experience auctions more actively and safely. It is also essential to ensure that the final participant who wins the right to participate can guarantee relatively high−quality data or computational performance. Therefore, a secure, necessary and effective mechanism is needed through strict theoretical proof and experimental verification. The traditional auction theory is mainly oriented to price, not giving quality issues as much consideration. Hence, it is challenging to discover the optimal mechanism and solve the privacy problem when considering multi−dimensional auctions. Therefore, we (1) propose a multi−dimensional information security mechanism, (2) propose an optimal mechanism that satisfies the Pareto optimality and incentive compatibility named the SecMDGM and (3) verify that for the aggregation model based on vertical data, this mechanism can improve the performance by 2.73 times compared to that of random selection. These are all important, and they complement each other instead of being independent or in tandem. Due to security issues, it can be ensured that the optimal multi−dimensional auction has practical significance and can be used in verification experiments. MDPI 2022-12-02 /pmc/articles/PMC9737689/ /pubmed/36502140 http://dx.doi.org/10.3390/s22239434 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Qian
Yao, Lin
Wang, Xuan
Lin Jiang, Zoe
Wu, Yulin
Ma, Tianzi
SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions
title SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions
title_full SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions
title_fullStr SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions
title_full_unstemmed SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions
title_short SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions
title_sort secmdgm: federated learning security mechanism based on multi−dimensional auctions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737689/
https://www.ncbi.nlm.nih.gov/pubmed/36502140
http://dx.doi.org/10.3390/s22239434
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