Enhancing Privacy and Collaborative Efforts in Healthcare through Federated Machine Learning

References

[1] M. J. Sheller, B. Edwards, G. A. Reina, J. Martin, S. Pati, A. Kotrotsou, et al., “Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data,” Sci. Rep., vol. 10, no. 1, Art. no. 12598, 2020. Nature

[2] I. Dayan, H. R. Roth, A. Zhong, A. Harouni, A. Gentili, A. Z. Abidin, et al., “Federated learning for predicting clinical outcomes in patients with COVID-19,” Nature Med., vol. 27, no. 10, pp. 1735–1743, 2021. Nature

[3] S. Pati, U. Baid, B. Edwards, M. Sheller, S. H. Wang, G. A. Reina, et al., “Federated learning enables big data for rare cancer boundary detection,” Nature Commun., vol. 13, no. 1, Art. no. 7346, 2022. Nature

[4] McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proc. 20th Int. Conf. Artif. Intell. Statist. (AISTATS), Apr. 2017, pp. 1273–1282. PMLR

[5] N. Rieke, J. Hancox, W. Li, F. Milletari, H. R. Roth, S. Albarqouni, et al., “The future of digital health with federated learning,” NPJ Digit. Med., vol. 3, no. 1, Art. no. 119, 2020. Nature

[6] L. Zhu, Z. Liu, and S. Han, “Deep leakage from gradients,” in Adv. Neural Inf. Process. Syst., vol. 32, 2019. NeurIPS

[7] R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in Proc. 2017 IEEE Symp. Security Privacy (SP), May 2017, pp. 3–18. IEEE

[8] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” Proc. Mach. Learn. Syst., vol. 2, pp. 429–450, 2020.

[9] J. Ogier du Terrail, S. S. Ayed, E. Cyffers, F. Grimberg, C. He, R. Loeb, et al., “FLamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings,” in Adv. Neural Inf. Process. Syst., vol. 35, pp. 5315–5334, 2022. NeurIPS

[10] K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, et al., “Practical secure aggregation for privacy-preserving machine learning,” in Proc. 2017 ACM SIGSAC Conf. Comput. Commun. Security, Oct. 2017, pp. 1175–1191. ACM

[11] Dwork and A. Roth, “The algorithmic foundations of differential privacy,” Found. Trends Theor. Comput. Sci., vol. 9, nos. 3–4, pp. 211–487, 2014. Foundations and Trends

[12] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proc. 2016 ACM SIGSAC Conf. Comput. Commun. Security, Oct. 2016, pp. 308–318. ACM

[13] G. A. Kaissis, M. R. Makowski, D. Rückert, and R. F. Braren, “Secure, privacy-preserving and federated machine learning in medical imaging,” Nature Mach. Intell., vol. 2, no. 6, pp. 305–311, 2020. Nature

[14] Ziller, T. T. Mueller, S. Stieger, L. F. Feiner, J. Brandt, R. Braren, et al., “Reconciling privacy and accuracy in AI for medical imaging,” Nature Mach. Intell., vol. 6, no. 7, pp. 764–774, 2024. Nature

@article{1723771,
      author = {Dharamdas Kumhar, Anil Kewat},
      title = {Enhancing Privacy and Collaborative Efforts in Healthcare through Federated Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {910-917},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723771.pdf},
      abstract = {Healthcare institutions create data from complementary electronic health records, medical images, laboratory measurements, genomic profiles, and connected devices. While aggregating this data can enhance machine-learning models, centralized collection increases exposure risks, conflicts with institutional data sovereignty, and is often constrained by legal, ethical, and operational challenges. Federated Machine Learning (FML) provides a collaborative framework where participating organizations can train a common model while keeping raw data stored locally. This paper presents a design-oriented review and a practical framework for privacy-aware, cross-silo FML in healthcare. The framework integrates local data governance, standardized preprocessing, federated optimization, secure aggregation, differential privacy, update validation, audit logging, and continuous clinical monitoring. It explicitly distinguishes data locality from formal privacy guarantees: while federated training minimizes raw data movement, model updates can still leak information or be subject to manipulation. An illustrative experiment involving five statistically heterogeneous synthetic hospitals evaluated centralized learning, isolated local learning, federated averaging, and clipped-and-noised federated averaging. The results indicate that beneficial collaboration is technically feasible, but privacy, fairness, interoperability, governance, and clinical validation must be engineered as integral parts of the joint lifecycle requirements rather than treated as consequences of federation alone.},
      keywords = {Collaborative healthcare, differential privacy, federated machine learning, healthcare artificial intelligence, secures aggregation.},
      month = {October},
  }