FSLBSC: Knowledge-Driven Federated Learning for Unbalanced Labeling AIS Signal Identification

With the rapid development of ubiquitous networks and unmanned devices, distributed unmanned systems have emerged. Applying distributed unmanned systems to AIS signal recognition helps address the challenges of identifying AIS signals in extreme environments such as remote seas and isolated regions. Nevertheless, the direct sharing of multi-domain sample data not only entails substantial costs but also poses a high risk of privacy information disclosure, significantly limiting the advancement of distributed unmanned systems in the area of AIS signal recognition. To address the above problems, this paper proposes a knowledge-driven federated semi-supervised learning based on similarity Consistency (FSLBSC). In detail, firstly, this approach achieves many-to-many cross-domain knowledge sharing while ensuring sample privacy and security, relying on knowledge-sharing federated learning. It resolves the issue of unbalanced sample data labels within a single domain and enables effective annotation of AIS signals through the secure sharing of multi-source knowledge. Results of simulation experiments demonstrate that the proposed method exhibits higher recognition accuracy for AIS signals when faced with unbalanced multi-domain data labeling. In the experiment using real AIS data gathered from four distinct sea surfaces, this approach obtained a global accuracy rate of 99.53%.