Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–318. https://doi.org/10.1145/2976749.2978318
Antunes, R. S., André da Costa, C., Küderle, A., Yari, I. A., & Eskofier, B. (2022). Federated learning for healthcare: Systematic review and architecture proposal. ACM Transactions on Intelligent Systems and Technology, 13(4), 1–23. https://doi.org/10.1145/3501813
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1175–1191. https://doi.org/10.1145/3133956.3133982
Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018). Federated learning of predictive models from federated electronic health records. International Journal of Medical Informatics, 112, 59–67. https://doi.org/10.1016/j.ijmedinf.2018.01.007
Choudhury, O., Gkoulalas-Divanis, A., Salonidis, T., Sylla, I., Park, Y., Hsu, G., & Das, A. (2019). Differential privacy-enabled federated learning for sensitive health data. arXiv. https://doi.org/10.48550/arXiv.1910.0257
Dalbeth, N., Merriman, T. R., & Stamp, L. K. (2016). Gout. The Lancet, 388(10055), 2039–2052. https://doi.org/10.1016/S0140-6736(16)00346-9
Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., Wang, C.-H., Hsu, C.-N., Lee, C. K., Ruan, P., Xu, D., Wu, D., Huang, E., Kitamura, F. C., Lacey, G., ... Li, Q. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27(10), 1735–1743. https://doi.org/10.1038/s41591-021-01506-3
Dehlin, M., Jacobsson, L., & Roddy, E. (2020). Global epidemiology of gout: Prevalence, incidence, treatment patterns and risk factors. Nature Reviews Rheumatology, 16(7), 380–390. https://doi.org/10.1038/s41584-020-0441-1
Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), 211–407. https://doi.org/10.1561/0400000042
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
FitzGerald, J. D., Dalbeth, N., Mikuls, T., Brignardello-Petersen, R., Guyatt, G., Abeles, A. M., Gelber, A. C., Harrold, L. R., Khanna, D., King, C., Levy, G., Libbey, C., Mount, D., Pillinger, M. H., Rosenthal, A., Singh, J. A., Sims, J. E., Smith, B. J., Wenger, N. S., ... Neogi, T. (2020). 2020 American College of Rheumatology guideline for the management of gout. Arthritis Care & Research, 72(6), 744–760. https://doi.org/10.1002/acr.24180
Gupta, G., Karuppiah, S., Dachepelly, S., & Verma, R. (2025). AI-driven data platforms: Real-time pipelines and governance. In 2025 International Conference on Sustainability, Innovation & Technology (ICSIT) (pp. 1–5). IEEE. https://doi.org/10.1109/ICSIT65336.2025.11294412
Kairouz, P., & McMahan, H. B. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2), 1–210. https://doi.org/10.1561/2200000083
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. arXiv. https://doi.org/10.48550/arXiv.1610.05492
Kumar, M., Kajal, M., Kumar, M., Kumar, R., & Kushwaha, P. (2025). Genomics-driven strategies for sustainable crop improvement in agriculture. In Genomics at the nexus of AI, computer vision, and machine learning (pp. 321–343).
Kumar, M., Dokania, A., Mangharamani, R., Garg, V., Jamili, L. K., & Kasetti, S. (2025). Improving diagnostic accuracy in telemedicine using image processing and pattern recognition techniques. In Proceedings of the International Conference on Engineering, Technology & Management (ICETM).
Kumar, M., Kumar, S., Jain, A., Kumar, S., & Dahiya, O. (2025). The role of generative models in modern healthcare: Applications, challenges, and implications. In Exploring generative adversarial networks and meta-learning synergies (pp. 215–232). IGI Global Scientific Publishing.
Li, D., & Wang, J. (2019). FedMD: Heterogeneous federated learning via model distillation. arXiv. https://doi.org/10.48550/arXiv.1910.03581
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/MSP.2020.2975749
Liu, Y.-R., Lin, C.-Y., Liu, Y.-Y., Patnaik, S., & Sharma, K. (2026). An explainable privacy-preserving federated learning framework for collaborative gout risk management using gene–lifestyle data. Journal of Information Technology Management, 12(1), 201–220.
Lundberg, S., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. arXiv. https://doi.org/10.48550/arXiv.1705.07874
Major, T. J., Dalbeth, N., Stahl, E. A., & Merriman, T. R. (2018). An update on the genetics of hyperuricaemia and gout. Nature Reviews Rheumatology, 14(6), 341–353. https://doi.org/10.1038/s41584-018-0004-
McMahan, B., Moore, E., Ramage, D., Hampson, S., & Agüera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54, 1273–1282.
Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2022). Federated learning for smart healthcare: A survey. ACM Computing Surveys, 55(3), 1–37. https://doi.org/10.1145/3501296
Sattler, F., Wiedemann, S., Müller, K.-R., & Samek, W. (2020). Robust and communication-efficient federated learning from non-IID data. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3400–3413. https://doi.org/10.1109/TNNLS.2019.2944481
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., & Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10(1), Article 12598. https://doi.org/10.1038/s41598-020-69250-1
Shukla, R. S. (2025). Enhancing privacy and efficiency techniques in federated learning systems: Applications in healthcare, finance, and smart devices. Journal of Information Technology Management, 17(Special Issue on SI: Intelligent Security and Management), 45–62.
Vidal, F. R., Gouveia, F., & Soares, C. (2022). Analysis of revocation mechanisms for blockchain applications and a proposed model based in self-sovereign identity. Journal of Information Technology Management, 14(Special Issue: The Business Value of Blockchain, Challenges, and Perspectives), 191–210.
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & 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
Xie, C., Koyejo, S., & Gupta, I. (2019). Asynchronous federated optimization. arXiv. https://doi.org/10.48550/arXiv.1903.03934
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 5(1), 1–19. https://doi.org/10.1007/s41666-020-00082-4
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19. https://doi.org/10.1145/3298981
Zhu, L., Liu, Z., & Han, S. (2019). Deep leakage from gradients. Advances in Neural Information Processing Systems, 32, 14774–14784.
