Blockchained Federated Learning for Internet of Things: A Comprehensive Survey

TL;DR

This survey reviews Blockchained Federated Learning (BlockFL) in IoT, highlighting its security, privacy, and decentralization benefits, while discussing challenges like overhead and domain-specific issues across various IoT sectors.

Contribution

It provides a comprehensive comparison of BlockFL models across IoT domains, analyzing their advantages, challenges, and potential technological enablers for secure distributed learning.

Findings

BlockFL enhances security and transparency in IoT model training.

Decentralization improves trust but introduces overhead concerns.

Different IoT domains face unique challenges like dynamic environments and identity management.

Abstract

The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data heterogeneity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.