Abstract
Supply chain management plays a vital role in the efficient and reliable movement of goods across various platforms, which involves several entities and processes. Ships in the supply chain are very important for the global economy to be connected. Detecting ships and their related activities is of paramount importance to ensure successful logistics and security. To improve logistics planning, security, and risk management, a strong framework is required that offers an efficient and privacy-preserving solution for identifying ships in supply chain management. In this paper, we propose a novel approach called Particle Swarm Optimization-enabled Federated Learning (PSO-FL) for ship detection in supply chain management. The proposed PSO-FL framework leverages the advantages of both Federated Learning (FL) and Particle Swarm Optimization (PSO) to address the challenges of ship detection in supply chain management. We can train a ship identification model cooperatively using data from several supply chain stakeholders, including port authorities, shipping firms, and customs agencies, thanks to the distributed nature of FL. By improving the choice of appropriate participants for model training, the PSO algorithm improves FL performance. We conduct extensive experiments using real-world ship data that is gathered from various sources to evaluate the effectiveness of our PSO-FL approach. The results demonstrate that our framework achieves superior ship detection accuracy of 94.88% compared to traditional centralized learning approaches and standalone FL methods. Furthermore, the PSO-FL framework demonstrates robustness, scalability, and privacy preservation, making it suitable for large-scale deployment in complex supply chain management scenarios.
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Supriya, Y. et al. (2024). PSO-Enabled Federated Learning for Detecting Ships in Supply Chain Management. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1962. Springer, Singapore. https://doi.org/10.1007/978-981-99-8132-8_31
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DOI: https://doi.org/10.1007/978-981-99-8132-8_31
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