Privacy-Preserving and Bandwidth-Efficient Federated Learning: An Application to In-Hospital Mortality Prediction - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

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

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.
Fichier principal
Vignette du fichier
paper.pdf (690.11 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03160473 , version 1 (05-03-2021)

Identifiers

Cite

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, ⟨10.1145/3450439.3451859⟩. ⟨hal-03160473⟩
195 View
580 Download

Altmetric

Share

Gmail Facebook X LinkedIn More