Privacy-Preserving Federated Brain Tumour Segmentation
Open accessAbstract
Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning algorithms, such as deep convolutional networks, which often require large numbers of diverse training examples. Federated learning sidesteps this difficulty by bringing code to the patient data owners and only sharing intermediate model training updates among them. Although a high-accuracy model could be achieved by appropriately aggregating these model updates, the model shared could indirectly leak the local training examples. In this paper, we investigate the feasibility of applying differential-privacy techniques to protect the patient data in a federated learning setup. We implement and evaluate practical federated learning systems for brain tumour segmentation on the BraTS dataset. The experimental results show that there is a trade-off between model performance and privacy protection costs.
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Medical data is particularly diverse, and inhomogeneous data distribution poses a challenge for federated learning algorithms and strategies, as many assume independently and identically distributed data; strategies such as FedAvg are prone to fail under these conditions, but recent results indicate that FL training is still feasible even if medical data is not uniformly distributed or includes a
“Medical data is particularly diverse—not only because of the variety of modalities, dimensionality and characteristics in general, but even within a specific protocol due to factors such as acquisition differences, brand of the medical device or local demographics.”
From The future of digital health with federated learning (2020) · cites this paper as [16]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredModels trained by federated learning can achieve performance levels comparable to ones trained on centrally hosted data sets and superior to models that only see isolated single-institutional data.
“Recent research has shown that models trained by FL can achieve performance levels comparable to ones trained on centrally hosted data sets and superior to models that only see isolated single-institutional data16,17.”
From The future of digital health with federated learning (2020) · cites this paper as [16]
The Results section (‘Federated vs. data-centralised training’; page not available in our copy) says the proposed federated procedure achieved comparable segmentation performance to centralised training. The Data preparation section describes clients using data from individual institutions, but the paper does not report a performance comparison showing that the federated model outperformed models trained only on single-institution data. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDecentralized data storage and federated learning systems have recently demonstrated results in medical imaging and genomics.
“with results in medical imaging 50,86 and genomics 87 recently demonstrated.”
From Secure, privacy-preserving and federated machine learning in medical imaging (2020) · cites this paper as [86]
The well-known trade-off between security and performance is also observed in benchmarking using this DP implementation.
“This well-known trade-off between security and performance [42] is also observed in benchmarking using this DP implementation [38].”
From A Pragmatic Framework for Federated Learning Risk and Governance in Academic Medical Centers (2026) · cites this paper as [38]
Models reached comparable performance when only 25% of weight updates were shared in a partial weight-sharing scheme.
“we showed that models can reach a comparable performance even when only 25% of the weight updates are shared”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [50]
FL was demonstrated for brain tumor segmentation.
“A complete brain part of an MRI [55] is helpful, and for brain tumor segmentation [56], the usage and advantages of FL were further demonstrated.”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [56]
Given the fact that there can be no comprehensive information distribution among institutions, FL can be trained using non-IID data.
“Given the fact that there can be no comprehensive information distribution among institutions [56, 62], FL can be trained using non-IID data.”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [56]
A general FL-trained model can achieve performance levels comparable to models trained on centrally hosted datasets and superior to models that only see isolated single-institutional data.
“Independently of the FL training strategy, a recent analysis for the medical field has shown that a general FL-trained model can achieve performance levels comparable to the ones trained on centrally hosted datasets and superior to models that only see isolated single-institutional data [27], [28].”
From Handling privacy-sensitive medical data with federated learning: Challenges and future directions (2023) · cites this paper as [27]
An FL model for federated brain imaging using DNNs for brain tumour segmentation achieves similar segmentation performance with the ideal centralized scheme while a degree of privacy protection is achieved.
“Simulations are conducted using a brain tumour dataset containing multiparametric pre-operative MRI scans of 285 subjects, indicating a similar segmentation performance with the ideal centralized scheme, while a degree of privacy protection is achieved.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [99]
FL is able to be deployed on medical devices such as MRI scan machine which has enough local dataset and reasonable computational resources to compute AI updates to join FL process with cloud server.
“Particularly, FL is able to be deployed on medical devices such as MRI scan machine [99] which has enough local dataset and reasonable computational resources to compute AI updates to join the FL process with the cloud server.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [99]
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“In particular, this is where privacy-preserving filters such as DP are implemented [38].”
- Authors
- Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., & Feng, A.
- Affiliations
- Nvidia (United Kingdom) (GB)
- King's College London (GB)
- Corresponding author
- Li, Wenqi
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Volume
- 11861
- Pages
- 133-141
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC7616593
- OpenAlex
- W2979637109
- Semantic Scholar
- 8d15862c9414b447e40b531fcd7ea378820bbd57
- Cited by
- 578
- References
- 10
- Citations by year
- 2026: 50 · 2025: 86 · 2024: 108 · 2023: 104 · 2022: 116 · 2021: 85 · 2020: 29
- FWCI
- 22.00
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia
- Wellcome Trust (203148, 213038)
- UK Research and Innovation
- National Institute for Health and Care Research
- King's College London (WT203148/Z/16/Z)
- Engineering and Physical Sciences Research Council ([WT203148/Z/16/Z], WT213038/Z/18/Z)
- Centre For Medical Engineering, King’s College London (WT 203148/Z/16/Z)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, differential privacy, brain tumor segmentation, medical data privacy, BraTS, privacy-accuracy trade-off, convolutional neural networks
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