Title: Merit: an on-demand IoT service delivery and resource scheduling scheme for federated learning and blockchain empowered 6G edge networks with reduced time and energy cost

Authors: Mahfuzulhoq Chowdhury

Addresses: Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong – 4349, Bangladesh

Abstract: Federated learning (FL) can improve the privacy-preserving issue of users' IoT devices, in which users complete the local training and transfer the updated model data to the central server for a global update. Due to high latency, the central server-based FL may suffer from huge energy loss at local user devices. MEC-based FL can improve the model accuracy and energy consumption at user devices via edge server-based task execution. Along with FL, blockchain can improve data security via permission-based access. Existing works explored only single type of IoT task without any appropriate resource scheduling for multiple tasks with different preferences, FL, and blockchain operations. This paper provides a merit-based resource scheduling scheme for different tasks with preferences, blockchain, and FL operations by checking resources, deadlines, delays, and resource costs. The simulation results verify that 45% running time and 53% cost gain is achieved in proposed scheme over the baseline schemes.

Keywords: blockchain; BC; federated learning; FL; resource scheduling; mobile edge computing; MEC; multi-task execution; task running time; utility; energy loss; resource utilisation cost; saturation throughput; trust score.

DOI: 10.1504/IJAHUC.2023.134603

International Journal of Ad Hoc and Ubiquitous Computing, 2023 Vol.44 No.2, pp.79 - 103

Received: 01 Dec 2022
Accepted: 10 May 2023
Published online: 30 Oct 2023 *

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