Federated Transfer Learning for Personalized Medicine: An AI-Driven Approach to Healthcare
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I3P102Keywords:
Federated Learning, Transfer Learning, Personalized Medicine, Data Heterogeneity, Privacy Preservation, Model Aggregation, Secure Aggregation, Explainability, IoT Integration, ScalabilityAbstract
Personalized medicine, which tailors medical treatment to individual patients based on their unique characteristics, is a promising frontier in healthcare. However, the development of personalized medicine is hindered by the scarcity of large, diverse datasets and the ethical and legal challenges associated with data sharing. Federated Transfer Learning (FTL) offers a solution by enabling the training of machine learning models across multiple decentralized data sources without the need for data to leave its original location. This paper explores the application of FTL in personalized medicine, highlighting its potential to enhance predictive accuracy, improve patient outcomes, and address privacy concerns. We present a comprehensive review of the current state of FTL in healthcare, discuss its technical challenges, and propose a novel FTL framework for personalized medicine. We also evaluate the performance of our proposed framework using real-world datasets and provide insights into its practical implications
References
[1] McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (pp. 1273-1282).
[2] Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.
[3] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.
[4] Kairouz, P., McMahan, H. B., & Song, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1-2), 1-210.
[5] Li, X., Liang, Y., & Zhang, Z. (2020). Federated transfer learning for healthcare applications. IEEE Transactions on Biomedical Engineering, 67(10), 2817-2828.
[6] Sheller, M. J., Edwards, B., Reina, G. A., & Bakas, S. (2018). Federated learning in healthcare: A review. arXiv preprint arXiv:1811.07216.
[7] Zhang, J., Liu, Y., & Yang, Q. (2019). Federated transfer learning for healthcare. IEEE Transactions on Knowledge and Data Engineering, 32(1), 1-13.
[8] Dwork, C. (2006). Differential privacy. In International Colloquium on Automata, Languages, and Programming (pp. 1-12). Springer.
[9] Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., ... & Sethi, R. (2017). Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1175-1191).
