Abstract
Digital Twin (DT) technology is pivotal in advancing smart underwater wireless IoT networks and effectively enhancing capabilities for monitoring and managing aquatic environments. For complex system-level DT models in these networks, assembling multiple unit-level DT models becomes crucial. Federated Learning (FL) presents a distributed machine learning paradigm that enables devices within underwater wireless IoT networks to collaboratively refine a DT model. Employing FL for DT modeling (FLDTM) is particularly valuable, as it allows for the enhancement of model accuracy without explicitly sharing local data, thereby preserving data privacy under challenging aquatic conditions. In this article, we propose a secure and efficient multi-server FL framework tailored for underwater wireless systems. We introduce a voting-based security prediction model to significantly bolster security in underwater wireless communication. Moreover, we introduce the network flow problem and employ a minimum-cost flow algorithm to enable FL servers’ cooperation. These strategies are integrated into a smart contract, namely, the UCB-based Smart Contract with a Security Prediction model and Minimum-Cost Flow (UCB-SCPF) policy. Experimental results show that the UCB-SCPF policy-based FLDTM framework achieves model accuracy comparable to ideal conditions while demonstrating excellent performance in terms of training efficiency and security. Additionally, the framework maintains stability as the network scale increases. These findings underscore the potential of the UCB-SCPF policy-based FLDTM framework in advancing DT technology for underwater wireless IoT networks.