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Conference Papers Year : 2023

Validation of Federated Unlearning on Collaborative Prostate Segmentation

Abstract

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.
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hal-04417106 , version 1 (26-01-2024)

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Yann Fraboni, Lucia Innocenti, Michela Antonelli, Richard Vidal, Laetitia Kameni, et al.. Validation of Federated Unlearning on Collaborative Prostate Segmentation. DECAF MICCAI 2023 Workshops, Medical Image Computing and Computer Assisted Intervention, Oct 2023, Toronto, Canada. pp.322-333, ⟨10.1007/978-3-031-47401-9_31⟩. ⟨hal-04417106⟩
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