Framework for blockchain-based federated learning integration into Port Community System : A Literature Review - SERROUKH , Bouchra; EL KHAZZAR , Aziz; RAKOMA , Stephen (Revue Internationale des Sciences de Gestion, 2025)
AI-based predictive analytics in the Port Community System (PCS) is critical for enhancing operational efficiency, resilience, and sustainability in the maritime supply chain. Recognizing this, the International Maritime Organization emphasizes integrating AI predictive analytics in the PCS as a value-added function. Synergy of blockchain and federated learning has emerged as a significant technology that can enhance predictive analytics by enabling secure, decentralized, and collaborative model training. However, there is currently no research integrating blockchain and FL within PCS. A systematic literature review identified 45 articles published between 2016 and 2025. Content analysis examined existing literature on blockchain and FL applications in Ports and supply chains, assessing the potential relevance and feasibility within PCS. The study finds that blockchain-FL applications in the supply chain remain in the exploratory stages, with no research currently investigating their Integration within PCS. This study makes a theoretical contribution by identifying the architectural requirements for implementing blockchain-FL in PSC. The proposed conceptual framework serves as a guide for further empirical research. It provides PCS stakeholders with insights into integrating ML functions into PCS, fostering a more intelligent, collaborative, and secure port ecosystem.