Federated learning for Spanish ports as an aid to digitization

• Autorzy: González Cancelas Nicoleta , Molina Serrano Beatriz , Soler Flores Francisco

Identyfikatory

DOI

10.2478/jok-2021-0018

• Języki publikacji: EN

Abstrakty

EN

The Spanish Port System is immersed in the process of digital transformation towards the concept of Ports 4.0. This entails new regulatory and connectivity requirements, making it necessary to implement the new technologies offered by the market towards digitalization. The digitalization of the individual processes in a first step helps the exchange of digital information between the members of the port community. The next step will mean that the information flow between the participants of a port community is done in a reliable, efficient, paperless way, and thanks to technologies. However, for the Spanish port sector, data exchange has a competitive disadvantage. That is why Federated Learning is proposed. This approach allows several organizations in the port sector to collaborate in the development of models, but without the need to directly share sensitive port data among themselves. Instead of gathering data on a single server, the data remains locked on your server, and the algorithms and predictive models travel between them. The goal of this approach is to benefit from a large set of data, which contributes to increased Machine Learning performance while respecting data ownership and privacy. Through an Inter-institution or "Crosssilo FL" model, different institutions contribute to the training with their local datasets in which different companies collaborate in training a learning machine for the discovery of patterns in private datasets of high sensitivity and high content. This environment is characterized by a smaller number of participants than the mobile case, with typically better bandwidth and less intermittency.

Słowa kluczowe

EN

machine learning; federated learning; digitalization

• Wydawca: Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONBiN
• Rocznik: 2021
• Tom: Vol. 51, iss. 2
• Strony: 1--17
• Opis fizyczny: Bibliogr. 57 poz., rys.

Twórcy

autor

González Cancelas Nicoleta

• Technical University of Madrid (Universidad Politécnica de Madrid)

autor

Molina Serrano Beatriz

• Technical University of Madrid (Universidad Politécnica de Madrid)

autor

Soler Flores Francisco

• Carlos III University of Madrid (Universidad Carlos III de Madrid)

Bibliografia

• 1. Acciaro M., Renken K., El Khadiri N.: Technological Change and Logistics Development in European Ports. In European Port Cities in Transition. Springer, Cham. 2020.
• 2. Alop A.: The main challenges and barriers to the successful "smart shipping". ransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 13. 2019.
• 3. Ashrafi M., Acciaro M., Walker T.R., Magnan G.M., Adams M.: Corporate sustainability in Canadian and US maritime ports. Journal of Cleaner Production, 220, 2019.
• 4. Baccelli O., Morino P.: The role of port authorities in the promotion of logistics integration between ports and the railway system: The Italian experience. Research in Transportation Business & Management, 100451. 2020.
• 5. Bakopoulou E., Tillman B., Markopoulou A.: A federated learning approach for mobile packet classification.arXiv preprint arXiv:1907.13113. 2019.
• 6. Bonawitz K., Eichner H., Grieskamp W., Huba D., Ingerman A., Ivanov V., Van Overveldt T.: Towards federated learning at scale: System design.arXiv preprint arXiv:1902.01046. 2019.
• 7. Castelein B., van Duin R., Geerlings H.: Identifying dominant stakeholder perspectives on sustainability issues in reefer transportation. A Q-method study in the Port of Rotterdam. Sustainability,11(12), 3425. 2019.
• 8. Chandiramani K., Garg D., Maheswari N.: Performance analysis of distributed and federated learning models on private data. Procedia Computer Science, 165, 2019.
• 9. da Silva V.L., Kovaleski J.L., Pagani R.N.: Technology transfer in the supply chain oriented to industry 4.0: a literature review. Technology Analysis & Strategic Management, 31(5), 2019.
• 10. Farooqui M., Gull H., Ilyas M., Iqbal S.Z., Khan M.A.A., Krishna G., Ahmed M.S.: Improving mental healthcare using a human centered internet of things model and embedding Homomorphic encryption scheme for cloud security. Journal of Computational and Theoretical Nanoscience,16(5-6), 2019.
• 11. Garcia-Alonso L., Monios J., Vallejo-Pinto J.Á.: Port competition through hinterland accessibility: the case of Spain. Maritime Economics & Logistics, 21(2), 2019.
• 12. Gesé Bordils M.D.M., González-Cancelas N., Serrano B.M.: Study of environmental sustainability in container terminals through KPI. World Scientific News, 145, 2020.
• 13. Gizelis C.A., Mavroeidakos T., Marinakis A., Litke A., Moulos V.: Towards a Smart Port: The Role of the Telecom Industry. In IFIP International Conference on Artificial Intelligence Applications and Innovations. Springer, Cham, June, 2020.
• 14. González-Cancelas N., Molina Serrano B., Esteban-Infantes M., Soler-Flores F., Camarero Orive A.: Escenario de digitalización para el Sistema Portuario Español. Revista Transporte y Territorio /22, 2020. DOI 10.34096/rtt.i22.6398.
• 15. Horn B.E., Nemoto T.: Intermodal Logistics Policies in the EU, the US and Japan. Transport Policy Studies' Review, 7(4), 2005.
• 16. Hu C., Jiang J., Wang Z.: Decentralized federated learning: a segmented gossip approach. arXiv preprint arXiv:1908.07782. 2019.
• 17. Ilin I., Jahn C., Weigell J., Kalyazina S.: Digital Technology Implementation for Smart City and Smart Port Cooperation. In International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI 2019). Atlantis Press. September, 2019.
• 18. Kairouz P., McMahan H.B., Avent B., Bellet A., Bennis M., Bhagoji A.N., d'Oliveira R.G.: Advances and open problems in federated learning.arXiv preprint arXiv:1912.04977. 2019.
• 19. Kakkad V., Patel M., Shah M.: Biometric authentication and image encryption for image security in cloud framework. Multiscale and Multidisciplinary Modeling, Experiments and Design, 2(4), 2019.
• 20. Karimireddy S.P., Kale S., Mohri M., Reddi S.J., Stich S.U., Suresh A.T. Scaffold: Stochastic controlled averaging for on-device federated learning. arXiv preprint arXiv:1910.06378. 2019.
• 21. Koh L., Dolgui A., Sarkis J.: Blockchain in transport and logistics-paradigms and transitions. 2020.
• 22. Kholod I., Yanaki E., Fomichev D., Shalugin E., Novikova E., Filippov E., Nordlund M.: Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis. Sensors, 21(1), 167, 2021
• 23. Li F.: The digital transformation of business models in the creative industries: A holistic framework and emerging trends. Technovation, 92, 102012. 2020.
• 24. Li T., Sahu A.K., Talwalkar A., Smith V.: Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 2020.
• 25. Lim W.Y.B., Luong N.C., Hoang D.T., Jiao Y., Liang Y.C., Yang Q., Miao C.: Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials. 2020.
• 26. Liu Y., Yuan X., Xiong Z., Kang J., Wang X., Niyato D.: Federated learning for 6g communications: Challenges, methods, and future directions. China Communications, 17(9), 2020.
• 27. Mańkowska M., Kotowska I., Pluciński M.: Seaports as Nodal Points of Circular Supply Chains: Opportunities and Challenges for Secondary Ports. Sustainability, 12(9), 3926, 2020.
• 28. Molavi A., Lim G.J., Race B.: A framework for building a smart port and smart port index. International Journal of Sustainable Transportation, 2019.
• 29. Molina Serrano B., González Cancelas N., Soler Flores F., Camarero Orive A.: Classification and prediction of port variables using Bayesian Networks. Transport Policy, vol. 67, 2017, DOI 10.1016/j.tranpol.2017.07.013.
• 30. Molina-Serrano B., Gonzalez-Cancelas N., Soler-Flores F.: Artificial intelligence model to analyze sustainability management of maritime ports. DYNA, vol. 93, no. 1, 2018. DOI http://dx.doi.org/10.6036/8508.
• 31. Molina Serrano B., Gonzalez-Cancelas N., Soler-Flores F.: Hacia la sostenibilidad portuaria mediante modelos probabilísticos: redes bayesianas. Informes de la Construcción, Vol. 70, 549, 2018. DOI 10.3989/id.54678.
• 32. Nascimento D.L.M., Alencastro V., Quelhas O.L.G., Caiado R.G.G., Garza-Reyes J.A., Rocha-Lona L., Tortorella G.: Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context. Journal of Manufacturing Technology Management. 2019.
• 33. Notteboom T., Lugt L.V.D., Saase N.V., Sel S., Neyens K.: The Role of Seaports in Green Supply Chain Management: Initiatives, Attitudes, and Perspectives in Rotterdam, Antwerp, North Sea Port, and Zeebrugge.Sustainability, 12(4), 1688, 2020.
• 34. Połap D., Srivastava G., Yu K.: Agent architecture of an intelligent medical system based on federated learning and blockchain technology. Journal of Information Security and Applications, 58, 102748, 2021.
• 35. Psomakelis E., Nikolakopoulos A., Marinakis A., Psychas A., Moulos V., Varvarigou T., Christou A.: A Scalable and Semantic Data as a Service Marketplace for Enhancing Cloud-Based Applications. Future Internet, 12(5), 77, 2020.
• 36. Ramaswamy S., Mathews R., Rao K., Beaufays F.: Federated learning for emoji prediction in a mobile keyboard.arXiv preprint arXiv:1906.04329, 2019.
• 37. Ren J., Wang H., Hou T., Zheng S., Tang C.: Federated learning-based computation offloading optimization in edge computing-supported internet of things. IEEE Access, 7, 2019.
• 38. Rodrigo González A., González-Cancelas N., Molina Serrano B., Camarero Orive A.: Preparation of a smart port indicator and calculation of a ranking for the Spanish Port System, Logistics, 4, 9; 2020, DOI 10.3390/logistics4020009.
• 39. Sakulyeva T., Kseniia Z.: The single window mechanism in the field of external sector of the economy. International Journal of Civil Engineering and Technology, 10(2), 2019.
• 40. Sánchez-Cambronero A., González-Cancelas N., Serrano B.M.: Analysis of port sustainability using the PPSC methodology (PESTEL, Porter, SWOT, CAME). World Scientific News, 146, 2020.
• 41. Sanders N.R., Boone T., Ganeshan R., Wood J.D.: Sustainable supply chains in the age of AI and digitization: research challenges and opportunities. Journal of Business Logistics, 40(3), 2019.
• 42. Saragiotis P.: Business process management in the port sector: a literature review. Maritime Business Review. 2019.
• 43. Sehnem S., Jabbour C.J.C., Pereira S.C.F., de Sousa Jabbour A.B.L.: Improving sustainable supply chains performance through operational excellence: circular economy approach. Resources, Conservation and Recycling, 149, 2019.
• 44. Smith J.S., Nebgen B.T., Zubatyuk R., Lubbers N., Devereux C., Barros K., Roitberg A.E.: Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nature communications, 10(1), 2019.
• 45. Szalavetz A.: Industry 4.0 and capability development in manufacturing subsidiaries. Technological Forecasting and Social Change, 145, 2019.
• 46. Tijan E., Agatić A., Jović M., Aksentijević S.: Maritime National Single Window-A Prerequisite for Sustainable Seaport Business. Sustainability, 11(17), 4570, 2019.
• 47. Tijan E., Jović M., Jardas M., Gulić M.: The Single Window concept in international trade, transport and seaports. Pomorstvo, 33(2), 2019.
• 48. Vial G.: Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 2019.
• 49. Wang X., Han Y., Wang C., Zhao Q., Chen X., Chen M.: In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Network, 33(5), 2019.
• 50. Wang H., Sreenivasan K., Rajput S., Vishwakarma H., Agarwal S., Sohn J.Y., Papailiopoulos D.: Attack of the Tails: Yes, You Really Can Backdoor Federated Learning.arXiv preprint arXiv:2007.05084. 2020.
• 51. Williams H.E., Bowman S.W., Jung J.T.: The limitations of government databases for analyzing fatal officer-involved shootings in the United States. Criminal Justice Policy Review, 30(2), 2019.
• 52. Xu J., Wang F.: Federated learning for healthcare informatics. arXiv preprint arXiv:1911.06270. 2019.
• 53. Yang Q., Liu Y., Chen T., Tong Y.: Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 2019.
• 54. Yavas V., Ozkan-Ozen Y.D.: Logistics centers in the new industrial era: A proposed framework for logistics center 4.0. Transportation Research Part E: Logistics and Transportation Review, 135, 101864. 2020.
• 55. Yoshida N., Nishio T., Morikura M., Yamamoto K., Yonetani R.: Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data.arXiv preprint arXiv:1905.07210. 2019.
• 56. Zhang C., Li S., Xia J., Wang W., Yan F., Liu Y.: BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning. In Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC 2020), April, 2020.
• 57. Zhao Y., Zhao J., Jiang L., Tan R., Niyato D., Li Z., Liu Y.: Privacy-preserving blockchain-based federated learning for IoT devices. IEEE Internet of Things Journal, 2020.