Secure Dynamic Scheduling for Federated Learning in Underwater Wireless IoT Networks Article Swipe
Lei Yan
,
Lei Wang
,
Guanjun Li
,
Jingwei Shao
,
Zhixin Xia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jmse12091656
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jmse12091656
Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, prediction, and classification in networks. In previous works, some online multi-armed bandit (MAB)-based FL frameworks were proposed to enable dynamic client scheduling for improving the efficiency of FL in underwater wireless IoT networks. However, the security of online dynamic scheduling, which is especially essential for underwater wireless IoT, is increasingly being questioned. In this work, we study secure dynamic scheduling for FL frameworks that can protect against malicious clients in underwater FL-assisted wireless IoT networks. Specifically, in order to jointly optimize the communication efficiency and security of FL, we employ MAB-based methods and propose upper-confidence-bound-based smart contracts (UCB-SCs) and upper-confidence-bound-based smart contracts with a security prediction model (UCB-SCPs) to address the optimal scheduling scheme over time-varying underwater channels. Then, we give the upper bounds of the expected performance regret of the UCB-SC policy and the UCB-SCP policy; these upper bounds imply that the regret of the two proposed policies grows logarithmically over communication rounds under certain conditions. Our experiment shows that the proposed UCB-SC and UCB-SCP approaches significantly improve the efficiency and security of FL frameworks in underwater wireless IoT networks.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmse12091656
- OA Status
- gold
- Cited By
- 1
- References
- 43
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4402716559Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/jmse12091656Digital Object Identifier
- Title
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Secure Dynamic Scheduling for Federated Learning in Underwater Wireless IoT NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-16Full publication date if available
- Authors
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Lei Yan, Lei Wang, Guanjun Li, Jingwei Shao, Zhixin XiaList of authors in order
- Landing page
-
https://doi.org/10.3390/jmse12091656Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/jmse12091656Direct OA link when available
- Concepts
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Computer science, Regret, Scheduling (production processes), Wireless, Wireless network, Upper and lower bounds, Internet of Things, Edge computing, Computer network, Distributed computing, Machine learning, Computer security, Mathematical optimization, Telecommunications, Mathematical analysis, MathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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| primary_location.raw_source_name | Journal of Marine Science and Engineering |
| primary_location.landing_page_url | https://doi.org/10.3390/jmse12091656 |
| publication_date | 2024-09-16 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2939352300, https://openalex.org/W2576683119, https://openalex.org/W4285040470, https://openalex.org/W4388705643, https://openalex.org/W2541884796, https://openalex.org/W2539190473, https://openalex.org/W2982464076, https://openalex.org/W4384028533, https://openalex.org/W4385627407, https://openalex.org/W4221143748, https://openalex.org/W3038426846, https://openalex.org/W3133599480, https://openalex.org/W6786822380, https://openalex.org/W3140722389, https://openalex.org/W4214545467, https://openalex.org/W2916008479, https://openalex.org/W2951832089, https://openalex.org/W2974429275, https://openalex.org/W4205507294, https://openalex.org/W4386858694, https://openalex.org/W4392939651, https://openalex.org/W3212539584, https://openalex.org/W4378530183, https://openalex.org/W3033632831, https://openalex.org/W3089495820, https://openalex.org/W2787083138, https://openalex.org/W2009551863, https://openalex.org/W2950929549, https://openalex.org/W2093562354, https://openalex.org/W2168405694, https://openalex.org/W2899381463, https://openalex.org/W2894446939, https://openalex.org/W3011070874, https://openalex.org/W3138597937, https://openalex.org/W3091870957, https://openalex.org/W2919115771, https://openalex.org/W2103496339, https://openalex.org/W2137983211, https://openalex.org/W3125537303, https://openalex.org/W4361799252, https://openalex.org/W2776768503, https://openalex.org/W3109504587, https://openalex.org/W3102449016 |
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