Communication-Efficient and Cross-Chain Empowered Federated Learning for Artificial Intelligence of Things

Abstract

Conventional machine learning approaches aggregate all training data in a central server, which causes massive communication overhead of data transmission and is also vulnerable to privacy leakage. Thereby, blockchain-based federated learning has emerged to protect Artificial Intelligence of Things (AIoT) devices from exposing their private data by the Federated Learning (FL) framework, and also enables decentralized model training without the vulnerability of a central server. However, the existing blockchain-based FL systems still suffer from (i) limited scalability of the single blockchain framework; and (ii) large communication costs incurred by iterative large-size model update transmission. To this end, we first design an efficient cross-chain framework for scalable and flexible model training management, in which multiple blockchains are customized for specific FL tasks and individually perform learning tasks for privacy protection. A cross-chain scheme is proposed to enable secure blockchain collaboration and interactions for efficient model training, model trading, and payment. We then propose a flexible model update compression scheme to save communication costs almost without compromising accuracy. Moreover, for model trading markets, we design a dynamic pricing scheme using machine learning-based auction for model trading. Numerical results demonstrate that the proposed framework and schemes achieve scalable, flexible, and communication-efficient decentralized FL systems in AIoT.