Data to 14 Oct 2026

Privacy-preserving Federated Brain Tumour Segmentation

arXiv 1910.00962 ↗First posted 2 Oct 2019cs.CVcited by 169

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Abstract (arXiv)

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.

Key facts. Cited by 169 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Oct 2019.

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Citing papers per year: 2021: 52; 2022: 46; 2023: 32; 2024: 25; 2025: 9; 2026: 5.

169 papers.

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2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
2021Federated Learning for Smart Healthcare: A Surveycited by 184
2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
2022Improving Generalization in Federated Learning by Seeking Flat Minimacited by 98
2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 84
2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 80
2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
2023Federated Learning for Medical Image Analysis: A Surveycited by 78
2021Collaborative Unsupervised Visual Representation Learning from Decentralized Datacited by 69
2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
2021Federated Multi-Armed Banditscited by 59
2021Decentralized Federated Learning through Proxy Model Sharingcited by 57

38 more

2022Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Modelscited by 53
2023Domain Generalization for Medical Image Analysis: A Reviewcited by 48
2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?cited by 44
2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
2022FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Drivingcited by 41
2021Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devicescited by 36
2023Federated Incremental Semantic Segmentationcited by 36
2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 35
2022Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learningcited by 35
2021Federated Contrastive Learning for Decentralized Unlabeled Medical Imagescited by 34
2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
2022Personalizing Federated Medical Image Segmentation via Local Calibrationcited by 29
2021PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakagecited by 28
2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 26
2021Resource-Efficient Federated Learningcited by 26
2021FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Predictioncited by 26
2022Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerabilitycited by 25
2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 23
2023GradientCoin: A Peer-to-Peer Decentralized Large Language Modelscited by 23
2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
2021Membership Inference Attacks on Deep Regression Models for Neuroimagingcited by 22
2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
2023Federated Learning for Computer Visioncited by 22
2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
2024FedFMS: Exploring Federated Foundation Models for Medical Image Segmentationcited by 21
2021EasyFL: A Low-code Federated Learning Platform For Dummiescited by 20
2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
2021FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentationcited by 19
2021Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentationcited by 19
2021Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Diseasecited by 19
2021Multi-task Federated Learning for Heterogeneous Pancreas Segmentationcited by 19

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