Optimizing Task Assignment for Reliable Blockchain-Empowered Federated Edge Learning

Abstract

A rapid-growing machine learning technique called federated edge learning has emerged to allow a massive number of edge devices (e.g. smart phones) to collaboratively train globally shared models without revealing their private raw data. This technique not only ensures good machine learning performance but also maintains data privacy of the edge devices. However, the federated edge learning still faces the following critical challenges: (i) difficulty in avoiding unreliable edge devices acting as workers for federated edge learning, and (ii) lack of efficient learning task assignment schemes among task publishers and workers. To tackle these challenges, reputation is utilized as a metric to evaluate the trustworthiness and reliability of the edge devices. A many-to-one matching model is proposed to address the task assignment problem between task publishers and reliable workers with high reputation. For stimulating reliable edge devices to join model training and enable secure reputation management, blockchain is employed to store the training records and manage reputation data in a decentralized and secure manner without the risk of a single point of failure. Numerical results show that the proposed schemes can achieve significant performance improvement in terms of reliability of federated edge learning.