Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review
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Abstract (arXiv)
Artificial Intelligence (AI) has demonstrated significant potential in automating various medical imaging tasks, which could soon become routine in clinical practice for disease diagnosis, prognosis, treatment planning, and post-treatment surveillance. However, the privacy concerns surrounding patient data present a major barrier to the widespread adoption of AI in medical imaging, as large, diverse training datasets are essential for developing accurate, generalizable, and robust Artificial intelligence models. Federated Learning (FL) offers a solution that enables organizations to train AI models collaboratively without sharing sensitive data. federated learning exchanges model training information, such as gradients, between the participating sites. Despite its promise, federated learning is still in its developmental stages and faces several challenges. Notably, sensitive information can still be inferred from the gradients shared during model training. Quantifying AI models' uncertainty is vital due to potential data distribution shifts post-deployment, which can affect model performance. Uncertainty quantification (UQ) in FL is particularly challenging due to data heterogeneity across participating sites. This review provides a comprehensive examination of FL, privacy-preserving FL (PPFL), and UQ in FL. We identify key gaps in current FL methodologies and propose future research directions to enhance data privacy and trustworthiness in medical imaging applications.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 55 references that are in the map. First posted on arXiv 24 Sep 2024.
Builds on
32 of 55 references.
References · 32
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
- 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
- 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
- 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
- 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
- 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 154
- 2013Learning by Transductioncited by 120
- 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
- 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
- 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
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- 2021OpenFL: An open-source framework for Federated Learningcited by 58
- 2021Decentralized Federated Learning through Proxy Model Sharingcited by 56
- 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
- 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 49
- 2023Federated Conformal Predictors for Distributed Uncertainty Quantificationcited by 46
- 2021A Bayesian Federated Learning Framework with Online Laplace Approximationcited by 41
- 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 38
- 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
- 2023Conformal Prediction for Federated Uncertainty Quantification Under Label Shiftcited by 29
- 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
- 2022TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernelscited by 26
- 2021Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learningcited by 23
- 2022APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learningcited by 20
- 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
- 2022Self-Aware Personalized Federated Learningcited by 16
- 2022DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Settingcited by 12
- 2022Federated Learning with Uncertainty via Distilled Predictive Distributionscited by 11
- 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
- 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scalercited by 6
- 2023Privacy Preserving Bayesian Federated Learning in Heterogeneous Settingscited by 2
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.