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Open AccessArticle
Towards Collaborative Edge Intelligence: Blockchain-Based Data Valuation and Scheduling for Improved Quality of Service †
1
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
2
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
3
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in International Conference on Computing, Networking and Communication (ICNC), Honolulu, HI, USA, 20–22 February 2023 .
Future Internet 2024, 16(8), 267; https://doi.org/10.3390/fi16080267 (registering DOI)
Submission received: 1 July 2024
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Revised: 23 July 2024
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Accepted: 24 July 2024
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Published: 28 July 2024
Abstract
Collaborative edge intelligence, a distributed computing paradigm, refers to a system where multiple edge devices work together to process data and perform distributed machine learning (DML) tasks locally. Decentralized Internet of Things (IoT) devices share knowledge and resources to improve the quality of service (QoS) of the system with reduced reliance on centralized cloud infrastructure. However, the paradigm is vulnerable to free-riding attacks, where some devices benefit from the collective intelligence without contributing their fair share, potentially disincentivizing collaboration and undermining the system’s effectiveness. Moreover, data collected from heterogeneous IoT devices may contain biased information that decreases the prediction accuracy of DML models. To address these challenges, we propose a novel incentive mechanism that relies on time-dependent blockchain records and multi-access edge computing (MEC). We formulate the QoS problem as an unbounded multiple knapsack problem at the network edge. Furthermore, a decentralized valuation protocol is introduced atop blockchain to incentivize contributors and disincentivize free-riders. To improve model prediction accuracy within latency requirements, a data scheduling algorithm is given based on a curriculum learning framework. Based on our computer simulations using heterogeneous datasets, we identify two critical factors for enhancing the QoS in collaborative edge intelligence systems: (1) mitigating the impact of information loss and free-riders via decentralized data valuation and (2) optimizing the marginal utility of individual data samples by adaptive data scheduling.
Keywords:
edge intelligence, blockchain, incentive mechanism, distributed machine learning, data valuation, curriculum learning, Internet of Things
edge intelligence, blockchain, incentive mechanism, distributed machine learning, data valuation, curriculum learning, Internet of Things
Share and Cite
MDPI and ACS Style
Du, Y.; Wang, Z.; Leung, C.; Leung, V.C.M.
Towards Collaborative Edge Intelligence: Blockchain-Based Data Valuation and Scheduling for Improved Quality of Service. Future Internet 2024, 16, 267.
https://doi.org/10.3390/fi16080267
AMA Style
Du Y, Wang Z, Leung C, Leung VCM.
Towards Collaborative Edge Intelligence: Blockchain-Based Data Valuation and Scheduling for Improved Quality of Service. Future Internet. 2024; 16(8):267.
https://doi.org/10.3390/fi16080267
Chicago/Turabian Style
Du, Yao, Zehua Wang, Cyril Leung, and Victor C. M. Leung.
2024. "Towards Collaborative Edge Intelligence: Blockchain-Based Data Valuation and Scheduling for Improved Quality of Service" Future Internet 16, no. 8: 267.
https://doi.org/10.3390/fi16080267
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