A Blockchain-Based Model Migration Approach for Secure and Sustainable Federated Learning in IoT Systems

Abstract

Model migration can accelerate model convergence during federated learning on the Internet of Things (IoT) devices and reduce training costs by transferring feature extractors from fast to slow devices, which, in turn, enables sustainable computing. However, malicious or lazy devices may migrate the fake models or resist sharing models for their benefit, reducing the desired efficiency and reliability of a federated learning system. To this end, this work presents a blockchain-based model migration approach for resource-constrained IoT systems. The proposed approach aims to achieve secure model migration and speed up model training while minimizing computation cost. We first develop an incentive mechanism considering the economic benefits of fast devices, which breaks the Nash equilibrium established by lazy devices and encourages capable devices to train and share models. Second, we design a clustering-based algorithm for identifying malicious devices and preventing them from defrauding incentives. Third, we use blockchain to ensure trustworthiness in model migration and incentive processes. Blockchain records the interaction between the central server and IoT devices and runs the incentive algorithm without exposing the devices’ private data. Theoretical analysis and experimental results show that the proposed approach can accelerate federated learning rates, reduce model training computation costs to increase sustainability, and resist malicious attacks.