Open Access
Issue |
ITM Web Conf.
Volume 76, 2025
Harnessing Innovation for Sustainability in Computing and Engineering Solutions (ICSICE-2025)
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|
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Article Number | 04002 | |
Number of page(s) | 9 | |
Section | Healthcare & Medicine | |
DOI | https://doi.org/10.1051/itmconf/20257604002 | |
Published online | 25 March 2025 |
- Fang, C., Dziedzic, A., Zhang, L., Oliva, L., Verma, A., Razak, F., Papernot, N., & Wang, B. (2024). Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data. arXiv preprint arXiv:2402.00205. [Google Scholar]
- Commey, D., Hounsinou, S., & Crosby, G. V. (2024). Securing health data on the blockchain: A differential privacy and federated learning framework. arXiv preprint arXiv:2405.11580. [Google Scholar]
- Ganadily, N. A., & Xia, H. J. (2024). Privacy preserving machine learning for electronic health records using federated learning and differential privacy. arXiv preprint arXiv:2406.15962. [Google Scholar]
- Xu, Y., Zhang, J., & Gu, Y. (2024). Privacy-preserving heterogeneous federated learning for sensitive healthcare data. arXiv preprint arXiv:2406.10563. [Google Scholar]
- Ren, H., Li, H., Liang, X., He, S., & Dai, Y. (2016). Privacy-enhanced and multifunctional health data aggregation under differential privacy guarantees. Sensors, 16(9), 1452. [Google Scholar]
- Zhao, P., Zhang, G., Wan, S., Liu, G., & Umer, T. (2020). A survey of local differential privacy for securing internet of vehicles. The Journal of Supercomputing, 76, 8643–8681. [Google Scholar]
- Ucci, D., Perdisci, R., Lee, J., & Ahamad, M. (2020). Privacy-preserving phone blacklisting using local differential privacy. In Annual Computer Security Applications Conference (pp. 1–12). [Google Scholar]
- Hu, Z., & Yang, J. (2020). Differential privacy protection method based on published trajectory cross correlation constraint. PLOS ONE, 15(8), e0237422. [Google Scholar]
- Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., … & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3, 119. [Google Scholar]
- Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., … & Pandey, G. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27, 1735–1743. [Google Scholar]
- Putra, K. T., Chen, H. C., Prayitno, Ogiela, M. R., & Chou, C. L. (2021). Federated compressed learning edge computing framework with ensuring data privacy for PM2.5 prediction in smart city sensing applications. Sensors, 21(1), 1–20. [Google Scholar]
- Guo, J., Mu, H., Liu, X., Ren, H., & Han, C. (2023). Federated learning for biometric recognition: A survey. Artificial Intelligence Review, 56, 1–35. [Google Scholar]
- Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2019). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 11(2), 1–26. [Google Scholar]
- Pokhrel, S. R., & Choi, D. (2019). Federated learning with blockchain for autonomous vehicles: Analysis and design challenges. In Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond (pp. 1–6). [Google Scholar]
- Elbir, A. M., & Coleri, S. (2020). Federated learning for vehicular networks. IEEE Transactions on Vehicular Technology, 69(12), 1–12. [Google Scholar]
- Liu, B., Wang, L., & Liu, M. (2019). Lifelong federated reinforcement learning: A learning architecture for navigation in cloud robotic systems. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1–6). [Google Scholar]
- Na, S., Roucek, T., Ulrich, J., Pikman, J., & Krajnik, T. (2020). Federated reinforcement learning for collective navigation of robotic swarms. IEEE Transactions on Cognitive and Developmental Systems, 13(3), 1–12. [Google Scholar]
- Yu, X., Queralta, J. P., & Westerlund, T. (2021). Towards lifelong federated learning in autonomous mobile robots with continuous sim-to-real transfer. Procedia Computer Science, 1–8. [Google Scholar]
- Roth, H. R., & Rieke, N. (2020). Federated learning for medical imaging. In Federated Learning (pp. 1–22). Springer. [Google Scholar]
- Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., & Bakas, S. (2020). Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10, 12598. [Google Scholar]
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