Privacy-preserving and bandwidth-efficient federated learning: an application to in-hospital mortality prediction
Open accessAbstract
Machine Learning, and in particular Federated Machine Learning, opens new perspectives in terms of medical research and patient care. Although Federated Machine Learning improves over centralized Machine Learning in terms of privacy, it does not provide provable privacy guarantees. Furthermore, Federated Machine Learning is quite expensive in term of bandwidth consumption as it requires participant nodes to regularly exchange large updates. This paper proposes a bandwidth-efficient privacy-preserving Federated Learning that provides theoretical privacy guarantees based on Differential Privacy. We experimentally evaluate our proposal for in-hospital mortality prediction using a real dataset, containing Electronic Health Records of about one million patients. Our results suggest that strong and provable patient-level privacy can be enforced at the expense of only a moderate loss of prediction accuracy.
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Even if raw data is not sent to the central server, exchanging model updates from massive IoMT devices is not bandwidth-efficient.
“The authors in [66] discuss that even the raw data is not needed to be sent to the central server, exchanging model updates from massive IoMT devices is not bandwidth-efficient.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [66]
The use of differential privacy to further enhance the privacy of FL systems is considered in many studies.
“The use of differential privacy to further enhance the privacy of FL systems is considered in many studies such as [62-64, 66].”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [66]
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Bhatnagar, P., Sodhi, M. S., Gururaj, H. L., Lavanya, M., & Raj, B. P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-18477-1_12
Hegde, G., & Bhat, R. (2026) · Cogent Engineering · doi:10.1080/23311916.2026.2678666
Dipto, S. M., Banerjee, S., Roy, S., Musawi, A. F. A., Ghosh, P., Shetty, S. S., & Rana, P. (2026) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3807503.3819489
Narula, M., Meena, J., & Vishwakarma, P. D. (2026) · Research Square · doi:10.21203/rs.3.rs-10801503/v1
- Authors
- Kerkouche, R., Ács, G., Castelluccia, C., & Genevès, P.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Budapest University of Technology and Economics (HU)
- Centre National de la Recherche Scientifique (FR)
- Venue
- Proceedings of the Conference on Health, Inference, and Learning
- Publisher
- ACM
- Published
- 2021-04-08
- Pages
- 25-35
- OpenAlex
- W3135725834
- Cited by
- 65
- References
- 36
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 14 · 2023: 18 · 2022: 7 · 2021: 2
- FWCI
- 4.74
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Health, Inference, and Learning (CHIL) (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- ANR (ANR-16-CE25-0010)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- federated learning, differential privacy, privacy-preserving machine learning, spectral efficiency, in-hospital mortality prediction, electronic health records
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