Federated unlearning for medical image analysis

Yuyao Zhong

doi:10.1117/12.3030004

8 May 2024 Federated unlearning for medical image analysis

Yuyao Zhong

Proceedings Volume 13162, Fourth Symposium on Pattern Recognition and Applications (SPRA 2023); 1316206 (2024) https://doi.org/10.1117/12.3030004
Event: Fourth Symposium on Pattern Recognition and Applications (SPRA2023), 2023, Napoli, Italy

Abstract

Recently, federated learning has gained significant attention for its ability to train models without centralizing clients’ data on a central server. This unique characteristic makes federated learning widely applicable in medical image analysis, a field where ensuring patients’ privacy is imperative for medical institutions. However, in compliance with privacy regulations in certain regions, medical institutions must mitigate the influence of their clients’ data on the global model. Existing machine unlearning methods cannot be straightforwardly applied in this scenario, as they require access to clients’ data. Therefore, federated unlearning becomes a necessary solution. The basic strategies of federated unlearning are excessively time-consuming to be practical, prompting an urgent need for a more cost-effective approach. While previous works have proposed various strategies, they often prove either too costly or unstable for real-world applicability. In this paper, we adopt an approach called importance-based selection based on FedEraser, which expedites the retraining process at the expense of storage space. We also attempt to enhance its storage efficiency by pruning less significant updates. We conducted experiments on two datasets in medical image analysis, and the results vividly demonstrate the effectiveness of removing the target client’s impacts. The time and storage consumption of our strategy are also consistent with expectations, emphasizing its practicality.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuyao Zhong "Federated unlearning for medical image analysis", Proc. SPIE 13162, Fourth Symposium on Pattern Recognition and Applications (SPRA 2023), 1316206 (8 May 2024); https://doi.org/10.1117/12.3030004

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KEYWORDS

Machine learning

Data modeling

Education and training

Medical imaging

Data privacy

Blood

Chest imaging

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