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Privacy-Preserving and Bandwidth-Efficient Federated Learning: An Application to In-Hospital Mortality Prediction

Raouf Kerkouche 1 Gergely Acs 2 Claude Castelluccia 1 Pierre Genevès 3
1 PRIVATICS - Privacy Models, Architectures and Tools for the Information Society
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
3 TYREX - Types and Reasoning for the Web
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : 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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https://hal.inria.fr/hal-03160473
Contributor : Tyrex Equipe <>
Submitted on : Friday, March 5, 2021 - 11:31:34 AM
Last modification on : Thursday, March 25, 2021 - 2:00:21 PM

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Raouf Kerkouche, Gergely Acs, Claude Castelluccia, Pierre Genevès. Privacy-Preserving and Bandwidth-Efficient Federated Learning: An Application to In-Hospital Mortality Prediction. CHIL 2021 - ACM Conference on Health, Inference, and Learning, Apr 2021, virtual event, France. pp.1-11. ⟨hal-03160473⟩

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