research-article

PiRATE: A Blockchain-Based Secure Framework of Distributed Machine Learning in 5G Networks

Authors:

Song GuoAuthors Info & Claims

IEEE Network, Volume 34, Issue 6

Pages 84 - 91

https://doi.org/10.1109/MNET.001.1900658

Published: 01 November 2020 Publication History

Abstract

in fifth-generation (5G) networks and beyond, communication latency and network bandwidth will be no longer be bottlenecks to mobile users. Thus, almost every mobile device can participate in distributed learning. That is, the availability issue of distributed learning can be eliminated. However, model safety will become a challenge. This is because the distributed learning system is prone to suffering from byzantine attacks during the stages of updating model parameters and aggregating gradients among multiple learning participants. Therefore, to provide the byzantine-resilience for distributed learning in the 5G era, this article proposes a secure computing framework based on the sharding technique of blockchain, namely PiRATE. To prove the feasibility of the proposed PiRATE, we implemented a prototype. A case study shows how the proposed PiRATE contributes to distributed learning. Finally, we also envision some open issues and challenges based on the proposed byzantine- resilient learning framework.

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Cited By

Chen HWang HLong QJin DLi Y(2024)Advancements in Federated Learning: Models, Methods, and PrivacyACM Computing Surveys10.1145/366465057:2(1-39)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3664650
Liu XLi JChen SJiang XYang FYang J(2024)Privacy-Preservation Robust Federated Learning with Blockchain-based Hierarchical FrameworkProceedings of the International Conference on Computing, Machine Learning and Data Science10.1145/3661725.3661726(1-6)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3661725.3661726
Zhang PDing SZhao Q(2024)Exploiting Blockchain to Make AI Trustworthy: A Software Development Lifecycle ViewACM Computing Surveys10.1145/361442456:7(1-31)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3614424
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Index Terms

PiRATE: A Blockchain-Based Secure Framework of Distributed Machine Learning in 5G Networks
1. Security and privacy
  1. Network security
  2. Security services

Index terms have been assigned to the content through auto-classification.

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Published In

cover image IEEE Network: The Magazine of Global Internetworking

IEEE Network: The Magazine of Global Internetworking Volume 34, Issue 6

November/December 2020

319 pages

ISSN:0890-8044

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IEEE Press

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Published: 01 November 2020

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Cited By

Chen HWang HLong QJin DLi Y(2024)Advancements in Federated Learning: Models, Methods, and PrivacyACM Computing Surveys10.1145/366465057:2(1-39)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3664650
Liu XLi JChen SJiang XYang FYang J(2024)Privacy-Preservation Robust Federated Learning with Blockchain-based Hierarchical FrameworkProceedings of the International Conference on Computing, Machine Learning and Data Science10.1145/3661725.3661726(1-6)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3661725.3661726
Zhang PDing SZhao Q(2024)Exploiting Blockchain to Make AI Trustworthy: A Software Development Lifecycle ViewACM Computing Surveys10.1145/361442456:7(1-31)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3614424
Zhu JCao JSaxena DJiang SFerradi H(2023)Blockchain-empowered Federated Learning: Challenges, Solutions, and Future DirectionsACM Computing Surveys10.1145/357095355:11(1-31)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3570953
Huang JKong LChen GXiang QChen XLiu X(2022)Blockchain-Based Federated Learning: A Systematic SurveyIEEE Network: The Magazine of Global Internetworking10.1109/MNET.129.220034637:6(150-157)Online publication date: 15-Nov-2022
https://dl.acm.org/doi/10.1109/MNET.129.2200346
Zhu XLi H(2021)Privacy-preserving Decentralized Federated Deep LearningProceedings of the ACM Turing Award Celebration Conference - China10.1145/3472634.3472642(33-38)Online publication date: 30-Jul-2021
https://dl.acm.org/doi/10.1145/3472634.3472642
Xu ZZhao LLiang WRana OZhou PXia QXu WWu G(2021)Energy-Aware Inference Offloading for DNN-Driven Applications in Mobile Edge CloudsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2020.303244332:4(799-814)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1109/TPDS.2020.3032443

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