Aono, Y., Hayashi, T., Wang, L., & Moriai, S. (2017). Privacy-preserving deep learning via additively homomorphic encryption. IEEE Transactions on Information Forensics and Security, 13(5), 1333-1345. https://doi.org/10.1109/TIFS.2017.2787987
Babai, M. Z., Boylan, J. E., & Rostami-Tabar, B. (2022). Demand forecasting in supply chains: A review of aggregation and hierarchical approaches. International Journal of Production Research, 60(1), 324-348. https://doi.org/10.1080/00207543.2021.2005268
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2020). How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics, 2938-2948.
Chen, J., Zhang, R., Wang, Y., & Liu, S. (2023). Privacy-preserving collaborative analytics for supply chain management using federated learning. Computers & Industrial Engineering, 176, 108968.
ElGamal, T. (1985). A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE transactions on information theory, 31(4), 469-472. https://doi.org/10.1109/TIT.1985.1057074
Fredrikson, M., Jha, S., & Ristenpart, T. (2015, October). Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security (pp. 1322-1333). https://doi.org/10.1145/2810103.2813677
Geyer, R. C., Klein, T., & Nabi, M. (2017). Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557.
Kairouz, P., McMahan, H. B., & Xiao, L. (2019). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1), 1-210. https://doi.org/10.1561/2200000083
Kairouz, P., McMahan, H. B., Avent, B., et al. (2023). Disconnected federated learning: Survey and open challenges. Journal of Machine Learning Research, 24, 1–62.
Kshetri, N., Dwivedi, Y. K., Davenport, T. H., & Panteli, N. (2023). Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. International Journal of Information Management, 102716. https://doi.org/10.1016/j.ijinfomgt.2023.102716
Li, T., Sanjabi, M., Beirami, A., & Smith, V. (2019). Fair resource allocation in federated learning. arXiv preprint arXiv:1905.10497.
Li, X., Chen, X., & Xu, L. (2020). Federated learning for supply chain management: A new approach for data privacy preservation. Journal of Supply Chain Management, 56(3), 15-30.
Lin, J., Du, M., & Liu, J. (2019). Free-riders in federated learning: Attacks and defenses. arXiv preprint arXiv:1911.12560.
Liu, H., Huang, G. Q., & Chen, Z. (2024). Federated learning-driven decision support systems for sustainable supply chains. International Journal of Logistics Research and Applications.
Lyu, L., Yu, H., Yang, Q., & Xu, X. (2024). Threats and countermeasures in federated learning: A comprehensive review. ACM Computing Surveys.
https://doi.org/10.1145/
McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017, April). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR.
Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7, 21. https://doi.org/10.3389/fnbot.2013.00021
Nguyen, D. C., Pham, Q. V., Pathirana, P. N., Ding, M., & Seneviratne, A. (2023). Federated learning for smart supply chains: A comprehensive survey. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/COMST.2023.
Seyedan, M., & Mafakheri, F.(2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 7, 53. https://doi.org/10.1186/s40537-020-00329-2
Shokri, R., & Shmatikov, V. (2015, October). Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security (pp. 1310-1321). https://doi.org/10.1145/2810103.2813687
Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research, 252(1), 1-26. https://doi.org/10.1016/j.ejor.2015.11.010
Tseng, Y. M., & Chen, F. G. (2011). A free-rider aware reputation system for peer-to-peer file-sharing networks. Expert Systems with Applications, 38(3), 2432-2440. https://doi.org/10.1016/j.eswa.2010.08.032
Wang, T., Li, Q., & Ren, S. (2024). Federated intelligence in digital supply chains: Architecture, applications, and challenges. International Journal of Production Economics, 261, 108905.
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., ... & Poor, H. V. (2020). Federated learning with differential privacy: Algorithms and performance analysis. IEEE transactions on information forensics and security, 15, 3454-3469. https://doi.org/10.1109/TIFS.2020.2988575
Zarei, G., Mohammad Khani, R., & Fathi, H. (2024). Investigating and identifying the consequences of using artificial intelligence in marketing. Management Research in Iran.
Zhang, Y., Chen, X., Li, J., & Poor, H. V. (2023). Secure and privacy-preserving federated learning: A survey. IEEE Transactions on Information Forensics and Security, 18, 1–17.
Zhao, Y., Xu, M., & Liu, Z. (2021). Machine learning in supply chain management: A systematic review. Computers & Industrial Engineering, 154, 107145.
Zhou, Y., Xiong, Z., Kang, J., & Niyato, D. (2023). Active federated learning: System design and performance analysis. IEEE Transactions on Neural Networks and Learning Systems.
