Federated Learning for Breast Density Classification: A Real-World Implementation

The Measurement of Observer Agreement for Categorical Data

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Mammographic Density and the Risk and Detection of Breast Cancer

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Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

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Privacy-Preserving Federated Brain Tumour Segmentation

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Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results

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Federated learning for predicting clinical outcomes in patients with COVID-19

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Federated learning for smart healthcare: A survey

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FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

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Privacy-preserving artificial intelligence in healthcare: Techniques and applications

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Federated learning for medical image analysis: A survey

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End-to-end privacy preserving deep learning on multi-institutional medical imaging

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Federated learning enables big data for rare cancer boundary detection

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Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

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Privacy preservation for federated learning in health care

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Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

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Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

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Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

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HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

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Federated Learning for Edge Computing: A Survey

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Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

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A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework

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Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.

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Federated Learning for Medical Image Analysis with Deep Neural Networks

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Specificity-Preserving Federated Learning for MR Image Reconstruction

Feng, C.-M., Yan, Y., Wang, S., Xu, Y. D., Shao, L., & Fu, H. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3202106 · Cited by 131

Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease

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Do Gradient Inversion Attacks Make Federated Learning Unsafe?

Hatamizadeh, A., Yin, H., Molchanov, P. A., Myronenko, A., Li, W., Dogra, P., Feng, A., Flores, M. G., Kautz, J., Xu, D., et al. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3239391 · Cited by 144

Federated Learning of Generative Image Priors for MRI Reconstruction

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Reviewing Federated Machine Learning and Its Use in Diseases Prediction

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A systematic review on federated learning in medical image analysis

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Memory-aware curriculum federated learning for breast cancer classification

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Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

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Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

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A comprehensive review on federated learning based models for healthcare applications

Sharma, S., & Guleria, K. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102691 · Cited by 111

Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data

Chen, Z., Yang, C., Zhu, M., Peng, Z., & Yuan, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3192483 · Cited by 98

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

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IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation

Jiang, M., Yang, H., Chen, C., & Dou, Q. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3263072 · Cited by 83

Secure neuroimaging analysis using federated learning with homomorphic encryption

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A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

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Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database

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Federated Semi-Supervised Medical Image Segmentation via Prototype-Based Pseudo-Labeling and Contrastive Learning

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Federated Learning in Dentistry: Chances and Challenges

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Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

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Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

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Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework

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The Federated Tumor Segmentation (FeTS) ChallengePreprint

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The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research

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Reliable customer analysis using federated learning and exploring deep-attention edge intelligence

Ahmed, U., Srivastava, G., & Lin, J. C.-W. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.08.028 · Cited by 52

Decentralized Learning in Healthcare: A Review of Emerging Techniques

Shiranthika, C., Saeedi, P., & Bajić, I. V. (2023) · IEEE Access · doi:10.1109/access.2023.3281832 · Cited by 55

Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning

Chen, Z., Zhu, M., Yang, C., & Yuan, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_33 · Cited by 47

Federated Learning For IoT: Applications, Trends, Taxonomy, Challenges, Current Solutions, and Future Directions

Adam, M., & Baroudi, U. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3506214 · Cited by 46

FedPerl: Semi-supervised Peer Learning for Skin Lesion Classification

Bdair, T. M., Navab, N., & Albarqouni, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_32 · Cited by 49

Privacy-Preserving Generative Adversarial Network for Case-Based Explainability in Medical Image Analysis

Montenegro, H., Silva, W., & Cardoso, J. S. (2021) · IEEE Access · doi:10.1109/access.2021.3124844 · Cited by 52

Privacy-preserving blockchain-based federated learning for brain tumor segmentation

Kumar, R., Bernard, C. M., Ullah, A., Khan, R. U., Kumar, J., Kulevome, D. K. B., Rao, Y., & Zeng, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108646 · Cited by 45

Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review

Uwimana, A., Gnecco, G., & Riccaboni, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109391 · Cited by 51

A Collaborative Federated Learning Framework for Lung and Colon Cancer Classifications

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FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Ho, T.-T., Tran, K.-D., & Huang, Y. (2022) · Sensors · doi:10.3390/s22103728 · Cited by 44

Federated Learning for Privacy-Preserved Medical Internet of Things

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Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data

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A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

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Privacy-Preserving Federated Learning in Healthcare

Moon, S., & Lee, W. H. (2023) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic57457.2023.10049966 · Cited by 42

Federated Learning in Computer Vision

Shenaj, D., Rizzoli, G., & Zanuttigh, P. (2023) · IEEE Access · doi:10.1109/access.2023.3310400 · Cited by 48

Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis

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Fedsld: Federated Learning with Shared Label Distribution for Medical Image Classification

Luo, J., & Wu, S. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761404 · Cited by 40

Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image SegmentationPreprint

Xia, Y., Yang, D., Li, W., Myronenko, A., Xu, D., Obinata, H., Mori, H., An, P., Harmon, S. A., Turkbey, E. B., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.10195 · Cited by 39

Federated Multi-Organ Segmentation With Inconsistent Labels

Xu, X., Deng, H., Gateño, J., & Yan, P. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3270140 · Cited by 37

Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification?

Yang, E., Li, M., Raghavan, S., Deng, F. M., Lang, M., Succi, M. D., Huang, A. J., & Kalpathy–Cramer, J. (2023) · British Journal of Radiology · doi:10.1259/bjr.20220769 · Cited by 36

CrowdGuard: Federated Backdoor Detection in Federated Learning

Rieger, P., Krauß, T., Miettinen, M., Dmitrienko, A., & Sadeghi, A.-R. (2024) · Proceedings 2024 Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.23233 · Cited by 36

MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers

Krauß, T., & Dmitrienko, A. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623212 · Cited by 35

Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare

Gawali, M., Arvind, C. S., Suryavanshi, S., Madaan, H., Gaikwad, A., Prakash, B., Kulkarni, V., & Pant, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80432-9_34 · Cited by 35

Handling data heterogeneity with generative replay in collaborative learning for medical imaging

Qu, L., Balachandar, N., Zhang, M., & Rubin, D. L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102424 · Cited by 35

FedBrain: A robust multi-site brain network analysis framework based on federated learning for brain disease diagnosis

Zhang, C., Meng, X., Liu, Q., Wu, S., Wang, L., & Ning, H. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126791 · Cited by 32

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

Jiang, M., Yang, H., Li, X., Liu, Q., Heng, P., & Dou, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_19 · Cited by 32

E2EGI: End-to-End Gradient Inversion in Federated Learning

Li, Z., Wang, L., Chen, G., Zhang, Z., Shafiq, M., & Gu, Z. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3204455 · Cited by 32

CPS Attack Detection under Limited Local Information in Cyber Security: An Ensemble Multi-Node Multi-Class Classification Approach

Liu, J., Tang, Y., Zhao, H., Wang, X., Li, F., & Zhang, J. (2023) · ACM Transactions on Sensor Networks · doi:10.1145/3585520 · Cited by 33

Federated adaptive reweighting for medical image classification

Ma, B., Feng, Y., Chen, G., Li, C., & Xia, Y. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109880 · Cited by 30

LEAF: A Federated Learning-Aware Privacy-Preserving Framework for Healthcare Ecosystem

Patel, N., Parekh, R., Amin, S. A., Gupta, R., Tanwar, S., Kumar, N., Iqbal, R., & Sharma, R. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3287393 · Cited by 28

Medical federated learning with joint graph purification for noisy label learning

Chen, Z., Li, W., Xing, X., & Yuan, Y. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102976 · Cited by 28

Tackling heterogeneity in medical federated learning via aligning vision transformers

Darzidehkalani, E., Shen, Y., Ou, Y., Sijtsema, N. M., & van Ooijen, P. M. A. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102936 · Cited by 27

Sensitivity-Aware Differential Privacy for Federated Medical Imaging

Zheng, L., Cao, Y., Yoshikawa, M., Shen, Y., Rashed, E. A., Taura, K., Hanaoka, S., & Zhang, T. (2025) · Sensors · doi:10.3390/s25092847 · Cited by 35

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks

Barrera, K., Rodellar, J., Alférez, S., & Merino, A. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107629 · Cited by 26

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

Ziegler, J., Pfitzner, B., Schulz, H., Saalbach, A., & Bert Arnrich, A. (2022) · Sensors · doi:10.3390/s22145195 · Cited by 30

Understanding Partnership Formation and Repeated Contributions in Federated Learning: An Analytical Investigation

Bi, X., Gupta, A., & Yang, M. (2023) · Management Science · doi:10.1287/mnsc.2023.00611 · Cited by 25

Federated Learning: Issues in Medical Application

Yoo, J. H., Jeong, H., Lee, J., & Chung, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91387-8_1 · Cited by 26

Federated Deep Learning for Wireless Capsule Endoscopy Analysis: Enabling Collaboration Across Multiple Data Centers for Robust Learning of Diverse Pathologies

Wahab, H., Mehmood, I., Ugail, H., Ser, J. D., & Muhammad, K. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.10.007 · Cited by 23

Lightweight and Dynamic Privacy-Preserving Federated Learning via Functional Encryption

Yu, B., Zhao, J., Zhang, K., Gong, J., & Qian, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3540312 · Cited by 27

Enhancing Alzheimer’s disease classification through split federated learning and GANs for imbalanced datasets

Nimeshika, G. N., & Subitha, D. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2459 · Cited by 23

GRACE: A Generalized and Personalized Federated Learning Method for Medical Imaging

Zhang, R., Fan, Z., Xu, Q., Yao, J., Zhang, Y., & Wang, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43898-1_2 · Cited by 22

Decentralised Federated Learning for Hospital Networks With Application to COVID-19 Detection

Giuseppi, A., Manfredi, S., Menegatti, D., Poli, C., & Pietrabissa, A. (2022) · IEEE Access · doi:10.1109/access.2022.3202922 · Cited by 23

Methods and Impact for Using Federated Learning to Collaborate on Clinical Research

Cheung, A. T. M., Nasir-Moin, M., Kwon, Y. J., Guan, J., Liu, C., Jiang, L. Y., Raimondo, C. D., Chotai, S., Chambless, L. B., Ahmad, H. S., et al. (2022) · Neurosurgery · doi:10.1227/neu.0000000000002198 · Cited by 22

Bilevel Hyperparameter Optimization and Neural Architecture Search for Enhanced Breast Cancer Detection in Smart Hospitals Interconnected With Decentralized Federated Learning Environment

Khan, S., Nosheen, F., Naqvi, S. S. A., Jamil, H., Faseeh, M., Khan, M. A., & Kim, D. (2024) · IEEE Access · doi:10.1109/access.2024.3392572 · Cited by 25

Federated Learning for Site Aware Chest Radiograph Screening

Chakravarty, A., Kar, A., Sethuraman, R., & Sheet, D. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433876 · Cited by 24

Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning

Krauß, T., König, J., Dmitrienko, A., & Kanzow, C. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.241366 · Cited by 20

Federation Opacity and the Promise of Federated Learning in Healthcare

Hatherley, J. J., Søgaard, A., Ballantyne, A., & Pauwels, R. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2637093 · Cited by 27

A Federated Learning Approach to Tumor Detection in Colon Histology Images

Gunesli, G. N., Bilal, M., Raza, S. E. A., & Rajpoot, N. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01994-5 · Cited by 20

IHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data

Tang, F., Liang, S., Ling, G., & Shan, J. (2023) · Cybersecurity · doi:10.1186/s42400-023-00166-9 · Cited by 20

Dense Contrastive-Based Federated Learning for Dense Prediction Tasks on Medical Images

Yang, Y., Liu, X., Gao, T., Xu, X., Zhang, P., & Wang, G. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3357947 · Cited by 20

Federated Learning with Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification

Weimann, K., & Conrad, T. O. F. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3427787 · Cited by 19

Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images

Gupta, C., Gill, N. S., Gulia, P., Alduaiji, N., Shreyas, J., & Shukla, P. K. (2025) · Scientific Reports · doi:10.1038/s41598-024-80187-7 · Cited by 22

A Privacy-Preserving Collaborative Federated Learning Framework for Detecting Retinal Diseases

Gulati, S., Guleria, K., Goyal, N., AlZubi, A. A., & Castilla, Á. K. (2024) · IEEE Access · doi:10.1109/access.2024.3493946 · Cited by 21

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge

Zenk, M., Baid, U., Pati, S., Linardos, A., Edwards, B., Sheller, M., Foley, P., Aristizábal, A., Zimmerer, D., Gruzdev, A. D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60466-1 · Cited by 29

Domain generalization of deep networks for medical image segmentation via meta learning

Liu, Q., Dou, Q., Chen, C., & Heng, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-399851-2.00016-8 · Cited by 14

Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning

Liu, W., Mo, J., & Zhong, F. (2023) · Applied Sciences · doi:10.3390/app13042109 · Cited by 13

Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images

Baptista, T., Soares, C., Oliveira, T., & Soares, F. (2023) · Applied Sciences · doi:10.3390/app132111620 · Cited by 16

Federated Learning: A Cross‐Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery

Lee, W., Hong, J., Lin, Y., Lu, Y., Hsu, Y., Lee, C., Yang, H., Wu, C., Lu, C., Sun, M., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28950 · Cited by 16

FedGuard: Selective Parameter Aggregation for Poisoning Attack Mitigation in Federated Learning

Chelli, M., Prigent, C., Schubotz, R., Costan, A., Antoniu, G., Cudennec, L., & Slusallek, P. (2023) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster52292.2023.00014 · Cited by 15

Transfer Learning in Breast Density Classification

Chowderpally, S. S., Sanagala, S. S., & Vedula, S. V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-1267-3_24

Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge

Schmidt, K., Bearce, B., Chang, K., Coombs, L. P., Farahani, K., Elbatel, M., Mouheb, K., Martí, R., Zhang, R., Zhang, Y., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103206 · Cited by 9

Towards generalizable Federated Learning in medical imaging: A real-world case study on mammography data

Tzortzis, I. N., Gutierrez-Torre, A., Sykiotis, S., Agulló, F., Bakalos, N., Doulamis, A. D., Doulamis, N. D., & Berral, J. L. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.031 · Cited by 27

Evaluating federated learning approaches for mammography under breast density heterogeneity

Quintana, G. I., Maria, F. M. D., & Vancamberg, L. (2026) · Journal of medical imaging · doi:10.1117/1.jmi.13.4.044504

MammoFL: Mammographic Breast Density Estimation using Federated LearningPreprint

Muthukrishnan, R., Heyler, A., Katti, K., Pati, S., Mankowski, W. C., Alahari, A., Sanborn, M., Conant, E. F., Scott, C. G., Winham, S. J., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.05575