Blockchain-Enabled and Multisignature-Powered Verifiable Model for Securing Federated Learning Systems

The Internet of Things is revolutionizing numerous industrial applications by employing smart devices in manufacturing and industrial processes. Industries based on IoT generate extensive data, typically analyzed using various Machine Learning (ML) models. Federated Learning (FL) is an emerging, privacy-preserving ML method where clients train models locally and develop a global model based on the aggregation of local models, without sharing the local dataset with a third party. However, federated learning methods struggle to achieve trustworthiness and to incorporate accountable ML principles. Blockchain technologies are being developed across different industries to enhance trust and security. This paper proposes a blockchain-enabled, verifiable model for securing federated learning within Internet of Things (IoT) systems. Our proposed framework combines a Trusted Execution Platform (TEE) to secure each client's local model training process, and multi-signature-powered global model verification to ensure machine learning model verifiability. We conducted several experiments with different datasets to assess our proposed framework. The experiments demonstrated the high efficiency and scalability of the proposed framework.