Towards Collaborative Edge Intelligence: Blockchain-Based Data Valuation and Scheduling for Improved Quality of Service

• Published in: Future internet Vol. 16; no. 8; p. 267
• Main Authors: Du, Yao, Wang, Zehua, Leung, Cyril, Leung, Victor C. M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Accuracy; Adaptive sampling; Algorithms; Artificial intelligence; Blockchain; Cloud computing; Collaboration; Curricula; curriculum learning; Data integrity; data valuation; Datasets; Devices; distributed machine learning; Distributed processing; Edge computing; edge intelligence; Efficiency; incentive mechanism; Internet of Things; Knapsack problem; Machine learning; Mobile computing; Network latency; Privacy; Quality of service; Quality of service architectures; Scheduling; Simulation methods; Swarm intelligence; Task scheduling; Valuation
• DOI: 10.3390/fi16080267
• ISSN: 1999-5903

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.