Validation of Federated Unlearning on Collaborative Prostate Segmentation
- Others:
- Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)
- E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
- King's College London
- Accenture Labs [Ireland]
- King's College Hospital (KCH)
- Medical Image Computing and Computer Assisted Intervention
Description
Machine Unlearning (MU) is an emerging discipline studying methods to remove the effect of a data instance on the parameters of a trained model. Federated Unlearning (FU) extends MU to unlearn the contribution of a dataset provided by a client wishing to drop from a federated learning study. Due to the emerging nature of FU, a practical assessment of the effectiveness of the currently available approaches in complex medical imaging tasks has not been studied so far. In this work, we propose the first in-depth study of FU in medical imaging, with a focus on collaborative prostate segmentation from multi-centric MRI dataset. We first verify the unlearning capabilities of a panel of FU methods from the state-of-the-art, including approaches based on model adaptation, differential privacy, and adaptive retraining. For each method, we quantify their unlearning effectiveness and computational cost as compared to the baseline retraining of a model from scratch after client dropout. Our work highlights a new perspective for the practical implementation of data regulations in collaborative medical imaging applications.
Abstract
International audience
Additional details
- URL
- https://inria.hal.science/hal-04417106
- URN
- urn:oai:HAL:hal-04417106v1
- Origin repository
- UNICA