Heterogeneous ship fuel oil consumption prediction at sea based on personalized federated learning

The precise prediction of ship Fuel Oil Consumption (FOC) at sea plays a crucial role in protecting the marine environment and reducing operational costs in the shipping industry. However, the data privacy of maritime vessels and Statistical heterogeneity of heterogeneous ships pose limitations on the predictive Performance of conven-tional Machine Learning (ML) methods. To address it, a method called Personalized Federated Learning (PFL) with CatBoost was proposed. Data from various sources including ship Information and sea condition data were mer-ged and cleaned to enhance data quality. CatBoost, a gradient boosting method for categorical features was applied to perform feature selection on local data, removing redundant information. The Federated learning with Personali-zation layers (FedPer) framework was introduced, incorporating a personalized layer to build a predictive model for heterogeneous ship FOC while ensuring data privacy. Furthermore, the basic layer's weight matrix was aggregated using the Federated Averaging algorithm (FedAvg) for parameter Updates and feedback, and the weight matrix for the personalized layer was optimized locally by client-side Deep Feedforward Neural Networks (DFNN) to mitigate the impact of data heterogeneity and improve prediction accuracy. Finally, comparative experiments were conducted using real-world examples of heterogeneous ship fuel oil consumption. The results demonstrated that the proposed method had achieved higher prediction accuracy compared to other models, and had practical significance for reduc-ing the heterogeneous ship FOC. © 2025 CIMS. All rights reserved.