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

cirpublications.com-osvo-1790880686-b293037132 1790880686 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2026 5 1 Federated Learning for Rare Disease Diagnosis and Research: A Systematic Review of Methods for Handling Extreme Data Scarcity, Class Imbalance, and Site Heterogeneity Luca Bianchi Marco Rossi Giulia Romano 2026 10.68159/b293037132 https://cirpublications.com/pub/journal/1/article/b293037132 Wang J, Ma F. Federated learning for rare disease detection: a survey. Rare Dis Orphan Drugs J. 2023;2(4):N-A. Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, et al. Federated learning enables big data for rare cancer boundary detection. Nat Commun. 2022;13(1):7346. Ankolekar A, Boie S, Abdollahyan M, Gadaleta E, Hasheminasab SA, Yang G, et al. Advancing breast, lung and prostate cancer research with federated learning: a systematic review. NPJ Digit Med. 2025;8(1):314. Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):119. Haripriya R, Khare N, Pandey M. Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings. Sci Rep. 2025;15(1):12482. Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W. Federated learning of predictive models from federated electronic health records. Int J Med Inform. 2018;112:59-67. Chang K, Balachandar N, Lam C, Yi D, Brown J, Beers A, et al. Distributed deep learning networks among institutions for medical imaging. J Am Med Inform Assoc. 2018;25(8):945-54. Adnan M, Kalra S, Cresswell JC, Taylor GW, Tizhoosh HR. Federated learning and differential privacy for medical image analysis. Sci Rep. 2022;12(1):1953. Huang L, Shea AL, Qian H, Masurkar A, Deng H, Liu D. Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J Biomed Inform. 2019;99:103291. Yan Z, Wicaksana J, Wang Z, Yang X, Cheng KT. Variation-aware federated learning with multi-source decentralized medical image data. IEEE J Biomed Health Inform. 2021;25(7):2615-28. Sarma KV, Harmon S, Sanford T, Roth HR, Xu Z, Tetreault J, et al. Federated learning improves site performance in multicenter deep learning without data sharing. J Am Med Inform Assoc. 2021;28(6):1259-64. Kairouz P, McMahan HB. Advances and open problems in federated learning. Found Trends Mach Learn. 2021;14(1-2):1-210. Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag. 2020;37(3):50-60. Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V. Federated optimization in heterogeneous networks. Proc Mach Learn Syst. 2020;2:429-50. Linardos A, Kushibar K, Walsh S, Gkontra P, Lekadir K. Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease. Sci Rep. 2022;12(1):3551. Yang D, Xu Z, Li W, Myronenko A, Roth HR, Harmon S, et al. Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan. Med Image Anal. 2021;70:101992. Li W, Milletarì F, Xu D, Rieke N, Hancox J, Zhu W, et al. Privacy-preserving federated brain tumour segmentation. In: International Workshop on Machine Learning in Medical Imaging. Cham: Springer; 2019. p. 133-41. Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, et al. Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans Inf Forensics Secur. 2020;15:3454-69. Lu MY, Chen RJ, Kong D, Lipkova J, Singh R, Williamson DF, et al. Federated learning for computational pathology on gigapixel whole slide images. Med Image Anal. 2022;76:102298. Wu N, Yu L, Yang X, Cheng KT, Yan Z. FedIIC: towards robust federated learning for class-imbalanced medical image classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer; 2023. p. 692-702. Yue G, Wei P, Zhou T, Song Y, Zhao C, Wang T, et al. Specificity-aware federated learning with dynamic feature fusion network for imbalanced medical image classification. IEEE J Biomed Health Inform. 2023;28(11):6373-83. Roth HR, Chang K, Singh P, Neumark N, Li W, Gupta V, et al. Federated learning for breast density classification: a real-world implementation. In: MICCAI Workshop on Domain Adaptation and Representation Transfer. Cham: Springer; 2020. p. 181-91. Abbas Q, Malik KM, Saudagar AK, Khan MB. Context-aggregator: an approach of loss- and class imbalance-aware aggregation in federated learning. Comput Biol Med. 2023;163:107167. Liu W, Mo J, Zhong F. Class imbalanced medical image classification based on semi-supervised federated learning. Appl Sci. 2023;13(4):2109. Dolaat KM, Erbad A, Ibrar M. Enhancing global model accuracy: federated learning for imbalanced medical image datasets. In: 2023 International Symposium on Networks, Computers and Communications (ISNCC). IEEE; 2023. p. 1-4. Andres J, Hilberger H, Hanke S, Bödenler M. Federated learning for healthcare: class imbalance mitigation and feature drift detection. In: IndHealth 2025. IOS Press; 2025. p. 292-7. Lee J, Sun J, Wang F, Wang S, Jun CH, Jiang X. Privacy-preserving patient similarity learning in a federated environment: development and analysis. JMIR Med Inform. 2018;6(2):e7744. Montalvo N, Requena F, Capriotti E, Rausell A. Federated learning for the pathogenicity annotation of genetic variants in multi-site clinical settings. Bioinformatics. 2025;41(10):btaf523.