Accelerating Blockchain-enabled Distributed Machine Learning by Proof of Useful Work

Yao Du, Cyril Leung, Zehua Wang, Victor C. M. Leung

Abstract: In Internet of Things (IoT) employing centralized machine learning, security is a major concern due to the heterogeneity of end devices. Decentralized machine learning (DML) with blockchain is a potential solution. However, blockchain with proof-of-work (PoW) consensus mechanism wastes computing resources and adds latency to DML. Computing resources can be utilized more efficiently with proof-of-useful-work (uPoW), which secures transactions by solving real-world problems. We propose a novel uPoW method that exploits PoW mining to accelerate DML through a task scheduling framework for multi-access edge computing (MEC) systems. To provide a good quality-of-service for the system, we minimize the latency by solving a multi-way number partitioning problem in the extended form. A novel uPoW-based mechanism is proposed to schedule DML tasks among MEC servers effectively. Simulation results show that our proposed blockchain strategies accelerate DML significantly compared with benchmarks.