Abstract
With the intelligentization of Maritime Transportation System (MTS), Internet of Thing (IoT) and machine learning technologies have been widely used to achieve the intelligent control and routing planning for ships. As an important branch of machine learning, federated learning is the first choice to train an accurate joint model without sharing ships' data directly. However, there are still many unsolved challenges while using federated learning in IoT-enabled MTS, such as the privacy preservation and Byzantine attacks. To surmount the above challenges, a novel mechanism, namely DisBezant, is designed to achieve the secure and Byzantine-robust federated learning in IoT-enabled MTS. Specifically, a credibility-based mechanism is proposed to resist the Byzantine attack in non-iid (not independent and identically distributed) dataset which is usually gathered from heterogeneous ships. The credibility is introduced to measure the trustworthiness of uploaded knowledge from ships and is updated based on their shared information in each epoch. Then, we design an efficient privacy-preserving gradient aggregation protocol based on a secure two-party calculation protocol. With the help of a central server, we can accurately recognise the Byzantine attackers and update the global model parameters privately. Furthermore, we theoretically discussed the privacy preservation and efficiency of DisBezant. To verify the effectiveness of our DisBezant, we evaluate it over three real datasets and the results demonstrate that DisBezant can efficiently and effectively achieve the Byzantine-robust federated learning. Although there are 40% nodes are Byzantine attackers in participants, our DisBezant can still recognise them and ensure the accurate model training.