DECENTRALIZED DEEP LEARNING IN HEALTHCARE: ADDRESSING DATA PRIVACY WITH FEDERATED LEARNING
Abstract
This study presents a privacy-preserving federated learning framework combining recurrent neural networks for healthcare applications, balancing data privacy with clinical utility. The decentralized system enables multi-institutional collaboration without centralized data collection, complying with HIPAA/GDPR through two technical safeguards: differential privacy via DP-SGD during local training and secure aggregation of model updates. Using LSTM/GRU architectures optimized for sequential medical data, the framework achieves an F1 Score of 67% with precision (60%) and recall (75%) suitable for clinical deployment, validated by Cohen's Kappa (40%) and Matthews Correlation Coefficient (40%). Experimental results using real-world datasets demonstrate the system's effectiveness in processing temporal patient records while maintaining data locality. The model reaches 07% of centralized accuracy despite privacy constraints, proving federated learning can deliver medically relevant performance without raw data sharing. The F1 Score above 0.75 with differential privacy confirms that rigorous privacy protections need not compromise predictive utility, while MCC values exceeding 0.4 indicate clinically meaningful performance for applications like readmission risk stratification. The work makes three primary contributions to medical AI: a functional FL-RNN implementation for sensitive health data, quantitative evidence of the privacy-utility tradeoff in clinical settings, and benchmarks for communication-efficient training across non-identical hospital datasets. These outcomes provide healthcare organizations with a practical template for developing collaborative AI that meets both clinical requirements and regulatory standards, particularly for time-sensitive applications involving electronic health records and vital sign monitoring. The framework's balanced performance across all evaluated metrics positions federated learning as a viable alternative to centralized approaches in privacy-sensitive healthcare environments.
References
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021). A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond. IEEE Internet of Things Journal, 8(7), 54765497. https://doi.org/10.1109/JIOT.2020.3030072 DOI: https://doi.org/10.1109/JIOT.2020.3030072
Akilo, Babalola E, Oyedotun, Samuel A, Oise, Godfrey P, Nwabuokei, Onyemaechi C, & Unuigbokhai, Nkem B. (2024). Intelligent Traffic Management System Using Ant Colony and Deep Learning Algorithms for Real-Time Traffic Flow Optimization. Journal of Science Research and Reviews, 1(2), 6371. https://doi.org/10.70882/josrar.2024.v1i2. 52 DOI: https://doi.org/10.70882/josrar.2024.v1i2.52
Aloi, G., Briante, O., Di Felice, M., Ruggeri, G., & Savazzi, S. (2017). The SENSE-ME platform: Infrastructure-less smartphone connectivity and decentralized sensing for emergency management. Pervasive and Mobile Computing, 42, 187208. https://doi.org/10.1016/j.pmcj.2017.10.004 DOI: https://doi.org/10.1016/j.pmcj.2017.10.004
Alsamhi, S. H., Myrzashova, R., Hawbani, A., Kumar, S., Srivastava, S., Zhao, L., Wei, X., Guizan, M., & Curry, E. (2024). Federated Learning Meets Blockchain in Decentralized Data Sharing: Healthcare Use Case. IEEE Internet of Things Journal, 11(11), 1960219615. https://doi.org/10.1109/JIOT.2024.3367249 DOI: https://doi.org/10.1109/JIOT.2024.3367249
Elayan, H., Aloqaily, M., & Guizani, M. (2021). Deep Federated Learning for IoT-based Decentralized Healthcare Systems. 2021 International Wireless Communications and Mobile Computing (IWCMC), 105109. https://doi.org/10.1109/IWCMC51323.2021.9498820 DOI: https://doi.org/10.1109/IWCMC51323.2021.9498820
Elayan, H., Aloqaily, M., & Guizani, M. (2022). Sustainability of Healthcare Data Analysis IoT-Based Systems Using Deep Federated Learning. IEEE Internet of Things Journal, 9(10), 73387346. https://doi.org/10.1109/JIOT.2021.3103635 DOI: https://doi.org/10.1109/JIOT.2021.3103635
Godfrey Perfectson Oise. (2023). A Framework on E-Waste Management and Data Security System. International Journal on Transdisciplinary Research and Emerging Technologies, 1(1).
Gu, M., Naraparaju, R., & Zhao, D. (2024). Enhancing Data Provenance and Model Transparency in Federated Learning SystemsA Database Approach (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2403.01451
Huang, C., Xu, G., Chen, S., Zhou, W., Ng, E. Y. K., & Albuquerque, V. H. C. D. (2022). An improved federated learning approach enhanced internet of health things framework for private decentralized distributed data. Information Sciences, 614, 138152. https://doi.org/10.1016/j.ins.2022.10.011 DOI: https://doi.org/10.1016/j.ins.2022.10.011
Kaissis, G. A., Makowski, M. R., Rckert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305311. https://doi.org/10.1038/s42256-020-0186-1 DOI: https://doi.org/10.1038/s42256-020-0186-1
Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021). Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges. IEEE Communications Surveys & Tutorials, 23(3), 17591799. https://doi.org/10.1109/COMST.2021.3090430 DOI: https://doi.org/10.1109/COMST.2021.3090430
Oise, G. (2023). A Web Base E-Waste Management and Data Security System. RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW, 1(1), 4955. https://doi.org/10.56778/rjslr.v1i1.113 DOI: https://doi.org/10.56778/rjslr.v1i1.113
Oise, G., & Konyeha, S. (2024). E-WASTE MANAGEMENT THROUGH DEEP LEARNING: A SEQUENTIAL NEURAL NETWORK APPROACH. FUDMA JOURNAL OF SCIENCES, 8(3), 1724. https://doi.org/10.33003/fjs-2024-0804-2579 DOI: https://doi.org/10.33003/fjs-2024-0804-2579
Oise, G. P., Nwabuokei, O. C., Akpowehbve, O. J., Eyitemi, B. A., & Unuigbokhai, N. B. (2025). TOWARDS SMARTER CYBER DEFENSE: LEVERAGING DEEP LEARNING FOR THREAT IDENTIFICATION AND PREVENTION. FUDMA JOURNAL OF SCIENCES, 9(3), 122128. https://doi.org/10.33003/fjs-2025-0903-3264 DOI: https://doi.org/10.33003/fjs-2025-0903-3264
Oise, G. P., Oyedotun, S. A., Nwabuokei, O. C., Babalola, A. E., & Unuigbokhai, N. B. (2025). ENHANCED PREDICTION OF CORONARY ARTERY DISEASE USING LOGISTIC REGRESSION. FUDMA JOURNAL OF SCIENCES, 9(3), 201208. https://doi.org/10.33003/fjs-2025-0903-3263 DOI: https://doi.org/10.33003/fjs-2025-0903-3263
Oise, G. P., & Susan, K. (2024). Deep Learning for Effective Electronic Waste Management and Environmental Health. https://doi.org/10.21203/rs.3.rs-4903136/v1 DOI: https://doi.org/10.21203/rs.3.rs-4903136/v1
Pati, S., Kumar, S., Varma, A., Edwards, B., Lu, C., Qu, L., Wang, J. J., Lakshminarayanan, A., Wang, S., Sheller, M. J., Chang, K., Singh, P., Rubin, D. L., Kalpathy-Cramer, J., & Bakas, S. (2024). Privacy preservation for federated learning in health care. Patterns, 5(7), 100974. https://doi.org/10.1016/j.patter.2024.100974 DOI: https://doi.org/10.1016/j.patter.2024.100974
Shiranthika, C., Saeedi, P., & Baji, I. V. (2023). Decentralized Learning in Healthcare: A Review of Emerging Techniques. IEEE Access, 11, 5418854209. https://doi.org/10.1109/ACCESS.2023.3281832 DOI: https://doi.org/10.1109/ACCESS.2023.3281832
Sun, W., Lei, S., Wang, L., Liu, Z., & Zhang, Y. (2021). Adaptive Federated Learning and Digital Twin for Industrial Internet of Things. IEEE Transactions on Industrial Informatics, 17(8), 56055614. https://doi.org/10.1109/TII.2020.3034674 DOI: https://doi.org/10.1109/TII.2020.3034674
Tian, Y., Wang, S., Xiong, J., Bi, R., Zhou, Z., & Bhuiyan, M. Z. A. (2024). Robust and Privacy-Preserving Decentralized Deep Federated Learning Training: Focusing on Digital Healthcare Applications. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(4), 890901. https://doi.org/10.1109/TCBB.2023.3243932 DOI: https://doi.org/10.1109/TCBB.2023.3243932
Wang, Z., Hu, Y., Yan, S., Wang, Z., Hou, R., & Wu, C. (2022). Efficient Ring-Topology Decentralized Federated Learning with Deep Generative Models for Medical Data in eHealthcare Systems. Electronics, 11(10), 1548. https://doi.org/10.3390/electronics11101548 DOI: https://doi.org/10.3390/electronics11101548
Wu, Q., He, K., & Chen, X. (2020). Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework. IEEE Open Journal of the Computer Society, 1, 3544. https://doi.org/10.1109/OJCS.2020.2993259 DOI: https://doi.org/10.1109/OJCS.2020.2993259
Yu, S., Muoz, J. P., & Jannesari, A. (2024). Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models (arXiv:2305.11414). arXiv. https://doi.org/10.48550/arXiv.2305.11414
Zacharis, G., Gadounas, G., Tsirtsakis, P., Maraslidis, G., Assimopoulos, N., & Fragulis, G. (2022). Implementation and Optimization of Image Processing Algorithm using Machine Learning and Image Compression. SHS Web of Conferences, 139, 03014. https://doi.org/10.1051/shsconf/202213903014 DOI: https://doi.org/10.1051/shsconf/202213903014
Zhang, Y., Bai, G., Li, X., Nepal, S., & Ko, R. K. L. (2021). Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2104.13050
Zhu, H., Wang, R., Jin, Y., Liang, K., & Ning, J. (2021). Distributed additive encryption and quantization for privacy preserving federated deep learning. Neurocomputing, 463, 309327. https://doi.org/10.1016/j.neucom.2021.08.062 DOI: https://doi.org/10.1016/j.neucom.2021.08.062
Copyright (c) 2025 FUDMA JOURNAL OF SCIENCES
This work is licensed under a Creative Commons Attribution 4.0 International License.
FUDMA Journal of Sciences
