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

The Measurement of Observer Agreement for Categorical Data

Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

Mammographic Density and the Risk and Detection of Breast Cancer

Boyd, N. F., Guo, H., Martin, L. J., Sun, L., Stone, J., Fishell, E., Jong, R. A., Hislop, G., Chiarelli, A. M., Minkin, S., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa062790 · Cited by 2,482

Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening

Pisano, E. D., Gatsonis, C. A., Hendrick, E. R., Yaffe, M. J., Baum, J. K., Acharyya, S., Conant, E. F., Fajardo, L. L., Bassett, L. W., D'Orsi, C. J., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa052911 · Cited by 1,937

Secure, privacy-preserving and federated machine learning in medical imaging

Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0186-1 · Cited by 1,676

Quantitative Classification of Mammographic Densities and Breast Cancer Risk: Results From the Canadian National Breast Screening Study

Boyd, N. F., Byng, J. W., Jong, R. A., Fishell, E., Little, L., Miller, A. B., Lockwood, G. A., Tritchler, D., & Yaffe, M. J. (1995) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/87.9.670 · Cited by 1,100

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

Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_9 · Cited by 609

Privacy-Preserving Federated Brain Tumour Segmentation

Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32692-0_16 · Cited by 578

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

Li, X., Gu, Y., Dvornek, N., Staib, L. H., Ventola, P., & Duncan, J. S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101765 · Cited by 480

Prevalence of Mammographically Dense Breasts in the United States

Sprague, B. L., Gangnon, R. E., Burt, V., Trentham‐Dietz, A., Hampton, J. M., Wellman, R., Kerlikowske, K. M., & Miglioretti, D. L. (2014) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/dju255 · Cited by 401

FedDropoutAvg: Generalizable federated learning for histopathology image classificationPreprint

Gunesli, G. N., Bilal, M., Raza, S. E. A., & Rajpoot, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.13230 · Cited by 13

Systematic review on privacy-preserving machine learning techniques for healthcare data

Sasirekha, B., & Gunavathi, C. (2025) · Journal of Cyber Security Technology · doi:10.1080/23742917.2025.2511145 · Cited by 16

Mitigating adversarial evasion attacks by deep active learning for medical image classification

Ahmed, U., Lin, J. C., & Srivastava, G. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11473-z · Cited by 13

Federated Learning Applications for Breast Cancer

Caroprese, L., Ruga, T., Vocaturo, E., & Zumpano, E. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385394 · Cited by 12

Federated learning-inspired smart ECG classification: an explainable artificial intelligence approach

Manocha, A., Sood, S. K., & Bhatia, M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20084-3 · Cited by 12

A novel federated deep learning scheme for glioma and its subtype classification

Ali, M. B., Gu, I. Y., Berger, M. S., & Jakola, A. S. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1181703 · Cited by 14

A Novel Federated Learning Framework for Sustainable and Efficient Breast Cancer Classification System (FL-L2CNN-BCDet)

El-Mawla, N. A., Berbar, M. A., El-Fishawy, N. A., & El-Rashidy, M. A. (2024) · IEEE Access · doi:10.1109/access.2024.3487767 · Cited by 13

Towards practical federated learning and evaluation for medical prediction models

Kazlouski, A., Perez, I. M., Noor, F. A., Högerman, M., Ettala, O. O., Pahikkala, T., & Airola, A. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106046 · Cited by 11

A COVID-19 Auxiliary Diagnosis Based on Federated Learning and Blockchain

Wang, Z., Cai, L., Zhang, X., Choi, C., & Su, X. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/7078764 · Cited by 11

Revealing Brain Tumor with Federated Learning

Caroprese, L., Ruga, T., Vocaturo, E., & Zumpano, E. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385865 · Cited by 11

Addressing catastrophic forgetting for medical domain expansionPreprint

Gupta, S., Singh, P., Chang, K., Qu, L., Aggarwal, M., Arun, N., Vaswani, A., Raghavan, S., Agarwal, V., Gidwani, M., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-1087025/v1 · Cited by 11

FeLebrities: A User-Centric Assessment of Federated Learning Frameworks

Riviera, W., Galazzo, I. B., & Menegaz, G. (2023) · IEEE Access · doi:10.1109/access.2023.3312579 · Cited by 10

Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles

Justaniah, E., Aldabbagh, G. A., Alhothali, A., & Abourokbah, N. (2022) · Applied Sciences · doi:10.3390/app12115599 · Cited by 10

Lung Cancer Detection via Federated Learning

Caroprese, L., Ruga, T., Vocaturo, E., & Zumpano, E. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385806 · Cited by 9

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

New ways to use imaging data in cardiovascular research: survey of opinions on federated learning and synthetic data

Williams, M. C., MacArthur, J. A. L., Forsyth, R., & Petersen, S. E. (2025) · European Heart Journal - Imaging Methods and Practice · doi:10.1093/ehjimp/qyaf012 · Cited by 9

Vertical federated learning based on data subset representation for healthcare application

Shi, Y., Zhang, J., Xue, M., Zeng, Y., Jia, G., Qi-hong, Y., & Li, M. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108623 · Cited by 11

Advances in Application of Federated Machine Learning for Oncology and Cancer Diagnosis

Nasajpour, M., Pouriyeh, S., Parizi, R. M., Han, M., Mosaiyebzadeh, F., Xie, Y., Liu, L., & Batista, D. M. (2025) · Information · doi:10.3390/info16060487 · Cited by 12

Federated Learning for Medical Image Classification: A Comprehensive Benchmark

Zhou, Z., Luo, G., Chen, M., Weng, Z., & Zhu, Y. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3631706 · Cited by 27

Federated learning framework integrating REFINED CNN and Deep Regression Forests

Nolte, D., Bazgir, O., Ghosh, S., & Pal, R. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad036 · Cited by 9

Semi-Supervised Federated Peer Learning for Skin Lesion Classification

Bdair, T. M., Navab, N., & Albarqouni, S. (2022) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2022-8g82 · Cited by 11

Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease

Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., & Lekadir, K. (2021) · doi:10.21203/rs.3.rs-688924/v1 · Cited by 9

Leveraging federated learning for boosting data privacy and performance in IVF embryo selection

Lee, C., Tzeng, C., Li, M., Lai, H.-H., Chen, C., Huang, Y., Chang, T. A., Chen, C.-H., Huang, C.-C., Lee, M., et al. (2024) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-024-03148-z · Cited by 8

Edge-Assisted Label-Flipping Attack Detection in Federated Learning

Alotaibi, N., Felemban, M., & Mahmood, S. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3496872 · Cited by 9

A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences

Mehedi, S. T., Abdulrazak, L. F., Ahmed, K., Uddin, M. S., Bui, F. M., Chen, L., Moni, M. A., & Al-Zahrani, F. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-89612-x · Cited by 9

Federated Learning Augmented with Convolutional Neural Networks for Brain Cancer Classification

Awotunde, J. B., Abikoye, C. O., Brahma, B., Oladipupo, E. T., & Bandyopadhyay, A. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.523 · Cited by 10

Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities

Malpetti, D., Scutari, M., Gualdi, F., van Setten, J., van der Laan, S. W., Haitjema, S., Lee, A. M., Hering, I., & Mangili, F. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1644291 · Cited by 12

Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana

Kades, K., Scherer, J., Zenk, M., Kempf, M., & Maier-Hein, K. H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18523-6_13 · Cited by 10

A Specificity-Preserving Generative Model for Federated MRI Translation

Dalmaz, O., Mirza, M. U., Elmas, G., Özbey, M., Dar, S. U. H., & Çukur, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18523-6_8 · Cited by 8

Artificial Intelligence and Machine Learning in Precision Health: An Overview of Methods, Challenges, and Future Directions

Bennett, R., Hemmati, M., Ramesh, R., & Razzaghi, T. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-74006-0_2 · Cited by 8

A Novel Approach to Breast Cancer Segmentation Using U-Net Model with Attention Mechanisms and FedProx

Gad, E., Khatwa, M. A., Elattar, M., & Selim, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48593-0_23 · Cited by 7

FLavourite: Horizontal and Vertical Federated Learning Setting Comparison

Riviera, W., Galazzo, I. B., & Menegaz, G. (2025) · IEEE Access · doi:10.1109/access.2025.3569158 · Cited by 6

Federated learning in healthcare applications

Kanhegaonkar, P., & Prakash, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-313233-9.00013-8 · Cited by 6

Enhancing Federated Learning Performance Fairness via Collaboration Graph-Based Reinforcement Learning

Xia, Y., Ma, B., Dou, Q., & Xia, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_25 · Cited by 6

Deep/Federated Learning Algorithms for Ultrasound Breast Cancer Image Enhancement

Waly, S. M., Taha, R., Ghany, M. A. A. E., & Salem, M. A. ‐. (2023) · doi:10.1109/icm60448.2023.10378931 · Cited by 6

A Survey of Image Gradient Inversion Against Federated LearningPreprint

Li, Z., Wang, L., Chen, G., Shafq, M., & Gu, Z. (2022) · doi:10.36227/techrxiv.18254723 · Cited by 6

Author Correction: Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake

Bajunaid, R., Niu, C., Hambly, C., Liu, Z., Yamada, Y., Alemán-Mateo, H., Anderson, L. J., Arab, L., Baddou, I., Bandini, L. G., et al. (2025) · Nature Food · doi:10.1038/s43016-025-01175-2 · Cited by 7

Exploring Uncertainty in Medical Federated Learning: A Survey

Zeng, X., Ahmed, A., & Tunio, M. H. (2025) · Electronics · doi:10.3390/electronics14204072 · Cited by 5

Multilevel thermoplastic waste segregation and classification with AHGSO using federated learning framework

Vignesh, R. S., & Subashini, M. M. (2024) · Kybernetes · doi:10.1108/k-07-2023-1210 · Cited by 6

Artificial Intelligence in Medicine and Privacy Preservation

Ziller, A., Passerat‐Palmbach, J., Trask, A., Braren, R., Rueckert, D., & Kaissis, G. A. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_261-1 · Cited by 6

Multi-organ segmentation of CT via convolutional neural network: impact of training setting and scanner manufacturer

Weisman, A. J., Huff, D. T., Govindan, R. M., Chen, S., & Perk, T. (2023) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/acfb06 · Cited by 5

Efficient Federated Tumor Segmentation via Normalized Tensor Aggregation and Client Pruning

Yin, Y., Yang, H., Liu, Q., Jiang, M., Chen, C., Dou, Q., & Heng, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09002-8_38 · Cited by 5

FedISM: Enhancing Data Imbalance via Shared Model in Federated Learning

Chung, W., Lin, Y., & Fang, S.-H. (2023) · Mathematics · doi:10.3390/math11102385 · Cited by 5

Federated Learning in Healthcare Industry: Mammography Case Study

Zielinski, K., Kowalczyk, N., Kocejko, T., Mazur-Milecka, M., Neumann, T., & Rumiński, J. (2023) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit58465.2023.10143132 · Cited by 5

Empowering Federated Learning for Massive Models with NVIDIA FLARE

Roth, H. R., Xu, Z., Hsieh, Y.-T., Renduchintala, A., Yang, I., Zhang, Z., Wen, Y., Yang, S., Lu, K. N., Kersten, K., et al. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-78841-3_1 · Cited by 13

Navigating real-world challenges: A case study on federated learning in computational pathology

Schoenpflug, L. A., Benavides, R. B., Nowak, M., Sheikhzadeh, F., Moayyedi, A., Wasag, K., Reimers, J., Zhou, M., Venugopal, R., Sobottka, B., et al. (2025) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100464 · Cited by 5

Análise do Aprendizado Federado em Redes Móveis

Bochie, K., Sammarco, M., Detyniecki, M., & Campista, M. E. M. (2021) · doi:10.5753/sbrc.2021.16712 · Cited by 4

Bio-Inspired Hyperparameter Tuning of Federated Learning for Student Activity Recognition in Online Exam Environment

Shankarappa, R., Prasad, N., Guddeti, R. M. R., & Mohan, B. R. (2024) · AI · doi:10.3390/ai5030051 · Cited by 3

FedEMG: Achieving Generalization, Personalization, and Resource Efficiency in EMG-Based Upper-Limb Rehabilitation Through Federated Prototype Learning

Lee, H., Jiang, M. X., Yang, Z., & Zhao, Q. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3593485 · Cited by 3

Weighting Schemes for Federated Learning in Heterogeneous and Imbalanced Segmentation Datasets

Otálora, S., Rafael‐Patiño, J., Madrona, A., Fischi-Gómez, E., Ravano, V., Kober, T., Christensen, S., Hakim, A., Wiest, R., Richiardi, J., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33842-7_4 · Cited by 3

Federated Semi-supervised Medical Image Segmentation Based on Asynchronous Transmission

Liu, F., & Yang, F. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-4749-2_5 · Cited by 3

Benchmarking Federated Learning Frameworks for Medical Imaging Tasks

Fonio, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51026-7_20 · Cited by 5

Detecting Lung Cancer with Federated and Transfer Learning

Mostafa, G., Hamidi, M. S., & Farid, D. M. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441256 · Cited by 4

Federated learning approach for human activity recognition in online examination environment

Ramu, S., Guddeti, R. M. R., & Mohan, B. R. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20815-0 · Cited by 3

Specialty Society Support for Multicenter Research in Artificial Intelligence

Allen, B., Schmidt, K., Brink, L., Pisano, E. D., Coombs, L. P., Apgar, C., Dreyer, K. J., & Wald, C. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.01.010 · Cited by 3

A Survey of Image Gradient Inversion Against Federated LearningPreprint

Li, Z., Wang, L., Chen, G., Shafq, M., & Gu, Z. (2022) · doi:10.36227/techrxiv.18254723.v1 · Cited by 3

Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models

Roth, H. R., Rieke, N., Albarqouni, S., & Li, Q. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3278528 · Cited by 3

Federated Deep Learning Approaches for Detecting Ocular Diseases in Medical Imaging: A Systematic Review

Gulati, S., Guleria, K., Goyal, N., & Dogra, A. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056400866250923175325 · Cited by 3

Advancing federated semi-supervised medical image segmentation: A duo of interactive denoising pseudo-labels and convolutional contrastive learning

Xu, Z., Zhang, Y., Li, B., Zhou, G., Lu, X., & Lukasiewicz, T. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104091 · Cited by 2

Privacy-Preserving Medical Image Sharing using Federated Learning and Watermarking

D.Kavitha, Mosses, A., Pitchandi., R., Sumiya, S., & Sukumar, T. (2026) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca68095.2026.11485538 · Cited by 2

Unfederated: Open Challenges, Deployment Gaps, and Emerging Directions in Federated Learning

Waref, D., Alayary, Y., Ghany, M. A. A. E., & Salem, M. A. ‐. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10696-3 · Cited by 2

Efficient Federated Tumor Segmentation via Parameter Distance Weighted Aggregation and Client Pruning

Jiang, M., Yang, H., Zhang, X., Zhang, S., & Dou, Q. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44153-0_16 · Cited by 2

Boosting Classification Tasks with Federated Learning: Concepts, Experiments and Perspectives

Hu, Y., & Chaddad, A. (2023) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe60311.2023.00031 · Cited by 2

A Comprehensive Survey on Federated Learning and its Applications in Health Care

Pacheco, S. A. B. (2024) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet62352.2024.10730687 · Cited by 2

Enabling Decentralized Clinical Insights

Gupta, S., Gupta, P., Goel, L., & Jain, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3306-9.ch006 · Cited by 2

Federated Synthetic Learning from Multi-institutional and Heterogeneous Medical DataPreprint

Chang, Q., Yan, Z., Qu, H. Y., Zhang, H., Baskaran, L., Zhang, S., & Metaxas, D. (2021) · Research Square · doi:10.21203/rs.3.rs-965641/v1 · Cited by 2

Federated Texture Classification: Implementing Colorectal Histology Image Analysis using Federated Learning

Bangare, J. L. (2024) · Journal of Electrical Systems · doi:10.52783/jes.698 · Cited by 2

The Role of PET Image Quantification in Modern Radiation Therapy

Abdelrahman, A., & Naqa, I. E. (2025) · PET Clinics · doi:10.1016/j.cpet.2025.07.008 · Cited by 1

Privacy-Preserving Skin Disease Detection Using One-Shot Federated Learning Approach

Anas, T. U. R., Razi, Q., -, S. B., Hassija, V., & Chalapathi, G. (2025) · Cognitive Computation · doi:10.1007/s12559-025-10531-0 · Cited by 1

The application of federated learning in breast cancer diagnosis and treatment: a survey

Wu, H., & Ghorbian, M. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11574-x · Cited by 1

Artificial Intelligence in Medicine and Privacy Preservation

Ziller, A., Passerat‐Palmbach, J., Trask, A., Braren, R., Rueckert, D., & Kaissis, G. A. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_261 · Cited by 1

Evaluating the Inclusion of Images with Artifacts in Medical Image Databases of Mammography for Machine Learning

Costa, H. A. D., Gurjão, E. C., & Ribeiro, V. T. R. (2023) · IEEE Latin American Conference on Computational Intelligence (LA-CCI) · doi:10.1109/la-cci58595.2023.10409478 · Cited by 1

An Analysis of Federated Learning Performance in Mobile Networks

Bochie, K., Sammarco, M., & Campista, M. E. M. (2023) · IEEE Virtual Conference on Communications (VCC) · doi:10.1109/vcc60689.2023.10474642 · Cited by 1

Diagnosis of Hepatocellular Carcinoma - HCC Liver Cancer Using Federated Learning on MR Images

Uzdur, B., Tekeli, E., İbrikçі, T., Rashid, H. U., & Ramachandran, G. (2025) · Çukurova Üniversitesi Mühendislik Fakültesi Dergisi · doi:10.21605/cukurovaumfd.1735231 · Cited by 1

RESPECTING PATIENT PRIVACY WITH FEDERATED ARTIFICIAL INTELLIGENCE

Ali, M. A. M., Aidid, E. M., & Abdullah, H. (2021) · Journal of Information Systems and Digital Technologies · doi:10.31436/jisdt.v3i2.220

An integrated framework for classifying mammograms according to BIRADS scale and breast tissue density.

Tzortzis, I. N., Sykiotis, S., Rallis, I., & Doulamis, N. D. (2023) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3594806.3596577

Data Domain Adaptation in Federated Learning in the Breast Mammography Image Classification Problem

Erimus, Ł., Borowska, A., Jaromin, A., Lewko, A., & Rumiński, J. (2024) · Conference on Human System Interactions · doi:10.1109/hsi61632.2024.10613534

A Diagnosis Model Based on Federated Learning for Lung Cancer Classification

Babu, A. M., & Bellamkonda, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-72284-4_13

Addressing Catastrophic Forgetting by Modulating Global Batch Normalization Statistics for Medical Domain Expansion

Gupta, S., Chang, K., Qu, L., Rana, A., Ahmed, S. R., Aggarwal, M., Arun, N., Vaswani, A., Raghavan, S., Agarwal, V., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73483-0_6

Bibliography

(2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323641-9.00026-1

Improving performance fairness in federated learning for heterogeneous medical images

Jiang, M., & Dou, Q. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323641-9.00016-9

Enhancing Data Privacy in Human Factors Studies with Federated Learning

Su, B., Qing, L., Lu, L., Jung, S., Fang, X., & Xu, X. (2025) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208251348025

PPsFLP: End-to-End Privacy Preserved and Secure Federated Learning Paradigm of Medical Images

Kaur, G. (2025) · SN Computer Science · doi:10.1007/s42979-025-04107-w

Skin cancer classification using novel fairness based federated learning algorithm

Karni, A., Abbas, Q., Ahmad, J., & Saudagar, A. K. J. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3171

Federated Learning in the Healthcare Industry

Mondal, R. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-99270-4_2

A review of research on federated learning in the field of medical image processing

Lei, Z., Wu, F., Zhang, J., & Wang, W. (2025) · doi:10.1145/3744504.3744508

Simulating Federated Learning for Enhanced Breast Cancer Detection in Ultrasound Images

Derecho, J., Caro, J. D. L., & Solano, G. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10827-2_31

Federated and Continual Learning of AI Models from Routine Clinical Data Under Privacy Constraints

Kollerathu, V. A., Bhombore, V. V., Ramesh, R., Tejani, A., Wolf, M., & Valadez, G. H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13961-0_11

Spider-Tailed Bitterling Optimisation enabled key generation for secure data storage for Internet of Medical Things in federated learning

Kanthasamy, D., & Kumar, C. N. S. V. (2026) · Australian Journal of Electrical & Electronics Engineering · doi:10.1080/1448837x.2025.2612429

NeuroScope: Trigger-Induced Activation Analysis for Backdoor Detection in Federated Learning

Yang, K., Xu, C., Xu, L., Zhang, P., & Zhang, R. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402518

FedSSV: Client Contribution for Secure Federated Learning Aggregation Using Scaled Shapley Values

Sani, S. D., Olapojoye, R., Salman, T., Ji, T., & Teixeira, M. A. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3674950 · Cited by 1

N Truths and a Lie: Consistency-Based Backdoor Defense for Vertical Federated Learning

Ma, X., Zhang, Z., He, K., Chen, J., & Du, R. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3675464

Privacy Preserved and Explainable Deep Medical Image Analysis: A Survey

Chowdhury, L., Thuseethan, S., Sebastian, Y., Rajasegarar, S., Alazab, M., & Yearwood, J. L. (2026) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2026.3675200

Secure Federated Learning Under Non-IID and Multimodal Breast Imaging

Rasheed, A. S. A., Awad, M., & Masud, M. M. (2026) · IEEE SoutheastCon · doi:10.1109/southeastcon63549.2026.11476171

Federated Learning in Healthcare: A Case Study on Covid-19

Karmakar, R., Sarkar, A., Malik, D., & Mondal, I. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-17184-9_1

A Practical Framework for Federated Fuzzy Clusterwise Regression, Preprocessing and Hyperparameter Tuning

Stallmann, M., Weiß, G., & Wilbik, A. (2026) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz69877.2026.11626378

Federated and Decentralized Machine Learning for Healthcare Systems: A Critical Review of Privacy-Preserving Technologies, Incentive Mechanisms, and Regulatory Compliance Frameworks

Conti, M., Greco, A., & Ferri, L. (2026) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/h352855186

Federated Learning for Rare Disease Diagnosis and Research: A Systematic Review of Methods for Handling Extreme Data Scarcity, Class Imbalance, and Site Heterogeneity

Bianchi, L., Rossi, M., & Romano, G. (2026) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/b293037132

FedBCFA: a boundary-constrained feature alignment federated learning framework for multi-center breast MRI classification

Du, W., Zhang, B., Cheng, L., & Li, X. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01280-7

Federated Learning-Based Multi-Task Framework for Privacy-Preserving Diagnosis of Muscle Invasion in Bladder Cancer

Zhang, Z., Cao, K., Li, J., He, Z., Zhang, J., Wang, G., Zhang, W., Song, W., & Huang, B. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02429-6

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