Suppressing Poisoning Attacks on Federated Learning for Medical Imaging

Alkhunaizi, Naif; Kamzolov, Dmitry; Takáč, Martin; Nandakumar, Karthik

doi:10.1007/978-3-031-16452-1_64

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…In addition, for Byzantine-tolerant FL, researchers employed a distance-based outlier suppression approach in which an aggregation server computes the cosine and Euclidean distances between distinct centre updates and assigns outlier scores to each centre. 39 Finally, the weighted average is calculated using the outlier ratings from each centre. On two medical imaging datasets (CheXpert and HAM10000), its outlier identification effectively protects against model poisoning attacks in both IID and Non-IID FL contexts.…”

Section: Research On Fl For Medical Imagingmentioning

confidence: 99%

Federated learning for medical imaging radiology

Rehman,

Hugo Lopez Pinaya,

Nachev

et al. 2023

The British Journal of Radiology

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Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the review outlines the future research directions considering various factors such as data, learning models, system design, governance, and human-in-loop to translate the SOTA into SOTP and effectively collaborate across multiple institutions.

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Section: Research On Fl For Medical Imagingmentioning

confidence: 99%