A Comprehensive Review on Federated Learning in Disease Detection

Year : 2026 | Volume : 03 | Issue : 01 | Page : 1 21
    By

    Projesh Saha,

  • Upasna Rai,

  • Disha Bhattacharjee,

  • Poulami Chhetri,

  1. Assistant Professor, Jakir Hossain Institute of Pharmacy, West Bengal, India
  2. Assistant Professor, Department of Pharmaceutical Analysis, Kaziranga University, Assam, India
  3. Research Scholar, Ecole Polytechnique, Palaiseau, France
  4. Research Scholar, Department of Agricultural Economics, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal, India

Abstract

Healthcare data, which is frequently dispersed among various organisations, has enormous potential to improve predictive analytics and illness identification. However, there are substantial privacy & legal obstacles to sharing this private data for centralised model training. Federated Learning is a paradigm shift that allows several organisations to work together to build a global model without disclosing raw patient information. Federated Learning uses a larger dataset to provide more reliable insights while maintaining individual privacy. The use of federated learning to improve disease diagnosis and predictive health analytics’ accuracy is demonstrated in this article. It investigates how, even in situations where data is localised at its source, Federated Learning architectures enable the creation of potent diagnostic and prognostic models. Federated Learning reduces the danger of data breaches and guarantees adherence to strict privacy laws such as the Health Insurance Portability and Accountability Act and the General Data Protection Regulation by encouraging collaborative learning and limiting data exposure. By overcoming the constraints imposed by the availability of data at a single site, this collaborative approach encourages the development of more accurate and generalised models. This article looks at a number of Federated Learning approaches that are relevant to the healthcare industry, including methods for managing system imbalances and data heterogeneity among participating nodes. It goes over real-world examples that show the way Federated Learning may be used to find illness biomarkers, forecast patient outcomes, and improve treatment regimens. It also discusses contemporary issues including model personalisation, communication overhead, and security flaws unique to healthcare federated learning. The article concludes by outlining potential research avenues and future directions, highlighting Federated Learning’s critical role in influencing the growth of the next wave of privacy-preserving, Artificial Intelligence- driven healthcare solutions.

Keywords: Federated Learning, disease detection, predictive analytics, logistic regression, Convolutional Neural Networks, Long Short-Term Memory Networks.

[This article belongs to Emerging Trends in Personalized Medicines ]

How to cite this article:
Projesh Saha, Upasna Rai, Disha Bhattacharjee, Poulami Chhetri. A Comprehensive Review on Federated Learning in Disease Detection. Emerging Trends in Personalized Medicines. 2026; 03(01):1-21.
How to cite this URL:
Projesh Saha, Upasna Rai, Disha Bhattacharjee, Poulami Chhetri. A Comprehensive Review on Federated Learning in Disease Detection. Emerging Trends in Personalized Medicines. 2026; 03(01):1-21. Available from: https://journals.stmjournals.com/etpm/article=2026/view=236749


References

  1. Fitzgerald RC, Antoniou AC, Fruk L, Rosenfeld N. The future of early cancer detection. Nat Med. 2022;28(4):666–77. Available from: https://www.nature.com/articles/s41591-022-01746-x.
  2. Crosby D, Bhatia S, Brindle KM, Coussens LM, Dive C, Emberton M, et al. Early detection of cancer. Science. 2022;375(6586). Available from: https://doi.org/10.1126/science.aay9040.
  3. O’Keeffe M, Barratt A, Fabbri A, Zadro JR, Ferreira GE, Sharma S, et al. Global media coverage of the benefits and harms of early detection tests. JAMA Intern Med. 2021;181(6):865–7. Available from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2778372.
  4. Utami TP, Adiwjaya S, Hasyim DM. The importance of early detection in disease management. J World Future Med Health Nurs. 2024;2(1):51–236. Available from: https://doi.org/10.55849/
    v2i1.692.
  5. Atkinson CF. ChatGPT and computational-based research: Benefits, drawbacks, and machine learning applications. Discov Artif Intell. 2023;3(1):1–18. Available from: https://link.springer .com/article/10.1007/s44163-023-00091-3.
  6. Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1). Available from: https://doi.org/10.1038 /s41746-020-00323-1.
  7. Yu T, Bagdasaryan E, Shmatikov V. Salvaging federated learning by local adaptation. 2020. Available from: http://arxiv.org/abs/2002.04758.
  8. Teo ZL, Jin L, Li S, Miao D, Zhang X, Ng WY, et al. Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture. Cell Rep Med. 2024;5(2). Available from: https://www.cell.com/action/showFullText?pii=S2666379124000429.
  9. Huang W, Li T, Wang D, Du S, Zhang J, Huang T. Fairness and accuracy in horizontal federated learning. Inf Sci. 2022;589:170-85. Available from: https://www.sciencedirect.com/science/article /abs/pii/S0020025521013244.
  10. Yang CC. Explainable artificial intelligence for predictive modeling in healthcare. J Healthc Inform Res. 2022;6(2):228–39. Available from: https://link.springer.com/article/10.1007/s41666-022-00114-1.
  11. Lopes J, Guimarães T, Santos MF. Predictive and prescriptive analytics in healthcare: A survey. Procedia Comput Sci. 2020;170:1029–34. Available from: https://www.sciencedirect.com/science /article/pii/S1877050920305159.
  12. Ramesh TR, Lilhore UK, Poongodi M, Simaiya S, Kaur A, Hamdi M. Predictive analysis of heart diseases with machine learning approaches. Malays J Comput Sci. 2022;2022(Special Issue 1):132–48. Available from: https://jpmm.um.edu.my/index.php/MJCS/article/view/35980.
  13. Srivastava D, Pandey H, Agarwal AK. Complex predictive analysis for health care: A comprehensive review. Bull Electr Eng Inform. 2023;12(1):521–31. Available from: https://mail.beei.org/index.php /EEI/article/view/4373.
  14. Wadhwa S, Babber K. Artificial intelligence in health care: predictive analysis on diabetes using machine learning algorithms. In: Lecture Notes in Computer Science. 2020;12250:354–66. Available from: https://link.springer.com/chapter/10.1007/978-3-030-58802-1_26.
  15. Guerrero MC, Parada JS, Espitia HE. EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Heliyon. 2021;7(6):e07258. Available from: https://www.cell.com/action/showFullText?pii= S240584402101361X.
  16. Leung CK, Fung DLX, Mushtaq SB, Leduchowski OT, Bouchard RL, Jin H, et al. Data science for healthcare predictive analytics. ACM Int Conf Proc Ser. 2020. Available from: https://dl.acm .org/doi/pdf/10.1145/3410566.3410598.
  17. Rajalakshmi V, Sasikala D, Kala A. A predictive analysis for heart disease using machine learning. In: Advances in Intelligent Systems and Computing. 2021;1172:473–9. Available from: https://link .springer.com/chapter/10.1007/978-981-15-5566-4_42.
  18. Dev S, Wang H, Nwosu CS, Jain N, Veeravalli B, John D. A predictive analytics approach for stroke prediction using machine learning and neural networks. Healthc Anal. 2022;2:100032. Available from: https://www.sciencedirect.com/science/article/pii/S2772442522000090.
  19. Sadilek A, Liu L, Nguyen D, Kamruzzaman M, Serghiou S, Rader B, et al. Privacy-first health research with federated learning. NPJ Digit Med. 2021;4(1):132. Available from: https://doi.org/10 .1038/s41746-021-00489-2.
  20. Adnan M, Kalra S, Cresswell JC, Taylor GW, Tizhoosh HR. Federated learning and differential privacy for medical image analysis. Sci Rep. 2022;12.
  21. Al-Manaseer H, Abualigah L, Alsoud AR, Zitar RA, Ezugwu AE, Jia H. A novel big data classification technique for healthcare application using support vector machine, random forest and J48. Stud Comput Intell. 2023;1071:205–15. Available from: https://link.springer.com/chapter /10.1007/978-3-031-17576-3_9.
  22. Hossen MN, Panneerselvam V, Koundal D, Ahmed K, Bui FM, Ibrahim SM. Federated machine learning for detection of skin diseases and enhancement of Internet of Medical Things (IoMT) security. IEEE J Biomed Health Inform. 2023;27(2):835–41.
  23. Sheller MJ, Edwards B, Reina GA, Martin J, Pati S, Kotrotsou A, et al. Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Sci Rep. 2020;10(1). https://doi.org/10.1038/s41598-020-69250-1.
  24. Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):119. https://doi.org/10.1038/s41746-020-00323-1.
  25. Lincy M, Kowshalya AM. Early detection of Type-2 diabetes using federated learning. Int J Sci Res Sci Eng Technol. 2020:257-67. https://doi.org/10.32628/IJSRSET207644.
  26. Zhang Y, Li Y, Wang Y, Wei S, Xu Y, Shang X. Federated learning–outcome prediction with multi-layer privacy protection. Front Comput Sci. 2024;18(6):1–10. Available from: https://link.springer.com/article/10.1007/s11704-023-2791-8.
  27. Kumarappan J, Rajasekar E, Vairavasundaram S, Kotecha K, Kulkarni A. Federated learning enhanced MLP–LSTM modeling in an integrated deep learning pipeline for stock market prediction. Int J Comput Intell Syst. 2024;17(1):1–15. Available from: https://link.springer.com/
    article/10.1007/s44196-024-00680-9.
  28. Shah D, Patel S, Bharti SK. Heart disease prediction using machine learning techniques. SN Comput Sci. 2020;1(6). https://doi.org/10.1007/s42979-020-00365-y.
  29. Garg A, Garg NB, Bansal M. Revolutionizing heart attack prevention: Machine learning models in smart healthcare. SN Comput Sci. 2025;6(2). https://doi.org/10.1007/s42979-025-03696-w.
  30. Abubakar JA, Odianose AE, Ademola OF. IoT-enabled machine learning for enhanced diagnosis of diabetes and heart disease in resource-limited settings. Lect Notes Data Eng Commun Technol. 2024;192:181–205.
  31. Sheller MJ, Reina GA, Edwards B, Martin J, Bakas S. Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. Brainlesion. 2019;11383:92–104. https://doi.org/10.1007/978-3-030-11723-8_9.
  32. Koonce B. EfficientNet. In: Convolutional Neural Networks with Swift for TensorFlow. 2021:109–23. Available from: https://link.springer.com/chapter/10.1007/978-1-4842-6168-2_10.
  33. 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):1–17. Available from: https://www.nature.com/articles/s41467-022-33407-5.
  34. Kumbhare S, Kathole AB, Shinde S. Federated learning aided breast cancer detection with intelligent heuristic-based deep learning framework. Biomed Signal Process Control. 2023;86:105080. Available from: https://www.sciencedirect.com/science/article/abs/pii/
    S174680942300513X.
  35. Almufareh MF, Tariq N, Humayun M, Almas B. A federated learning approach to breast cancer prediction in a collaborative learning framework. Healthcare. 2023;11(24):3185. Available from: https://www.mdpi.com/2227-9032/11/24/3185/htm.
  36. Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021;27(10):1735–43. https://doi.org/10.1038/s41591-021-01506-3.
  37. Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021;27(10):1735–43. Available from: https://www.nature.com/articles/s41591-021-01506-3.
  38. Chowdhury D, Banerjee S, Sannigrahi M, Chakraborty A, Das A, Dey A, et al. Federated learning-based COVID-19 detection. Expert Syst. 2023;40(5):e13173. Available from: https://doi.org/10.1111/exsy.13173.
  39. Naz S, Phan KT, Chen YPP. A comprehensive review of federated learning for COVID-19 detection. Int J Intell Syst. 2022;37(3):2371–92. Available from: https://doi.org/10.1002/int.22777.
  40. Wu X, Hui H, Niu M, Li L, Wang L, He B, et al. Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study. Eur J Radiol. 2020;128:109041. Available from: https://www.sciencedirect.com/science/article/pii/S0720048X20302308.
  41. Shankar K, Perumal E. A novel hand-crafted with deep learning features-based fusion model for COVID-19 diagnosis and classification using chest X-ray images. Complex Intell Syst. 2021;7(3):1277–93. Available from: https://link.springer.com/article/10.1007/s40747-020-00216-6.
  42. Lakhan A, Grønli TM, Muhammad G, Tiwari P. EDCNNS: Federated learning enabled evolutionary deep convolutional neural network for Alzheimer disease detection. Appl Soft Comput. 2023;147:110804. Available from: https://www.sciencedirect.com/science/article/abs
    /pii/S1568494623008220.
  43. Ducange P, Marcelloni F, Renda A, Ruffini F. Federated learning of XAI models in healthcare: A case study on Parkinson’s disease. Cognit Comput. 2024;16(6):3051–76. Available from: https://link.springer.com/article/10.1007/s12559-024-10332-x.
  44. Khan MA, Alsulami M, Yaqoob MM, Alsadie D, Saudagar AKJ, AlKhathami M, et al. Asynchronous federated learning for improved cardiovascular disease prediction using artificial intelligence. Diagnostics. 2023;13(14):2340. Available from: https://www.mdpi.com/2075-4418 /13/14/2340/htm.
  45. Gupta M, Kumar M, Gupta Y. A blockchain-empowered federated learning-based framework for data privacy in lung disease detection system. Comput Human Behav. 2024;158:108302. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0747563224001705.
  46. Narayanan VS, M SV, Ahmed SA, J G. Mobile application for oral disease detection using federated learning. arXiv. 2023. Available from: https://arxiv.org/pdf/2403.12044.
  47. Luo G, Liu T, Lu J, Chen X, Yu L, Wu J, et al. Influence of data distribution on federated learning performance in tumor segmentation. Radiol Artif Intell. 2023;5(3). Available from: https://doi.org /10.1148/ryai.220082.
  48. Zhang W, Jin W, Rho S, Jiang F, Yang CF. A federated learning framework for brain tumor segmentation without sharing patient data. Int J Imaging Syst Technol. 2024;34(4):e23147. Available from: https://doi.org/10.1002/ima.23147.
  49. Rajagopal A, Redekop E, Kemisetti A, Kulkarni R, Raman S, Sarma K, et al. Federated learning with research prototypes: Application to multi-center MRI-based detection of prostate cancer with diverse histopathology. Acad Radiol. 2023;30(4):644–57. Available from: https://www .sciencedirect.com/science/article/pii/S1076633223000697.
  50. Heidari A, Javaheri D, Toumaj S, Navimipour NJ, Rezaei M, Unal M. A new lung cancer detection method based on chest CT images using federated learning and blockchain systems. Artif Intell Med. 2023;141:102572. Available from: https://www.sciencedirect.com/science/article/abs/pii /S0933365723000866.
  51. Wenzel HHB, Hardie AN, Moncada-Torres A, Høgdall CK, Bekkers RLM, Falconer H, et al. A federated approach to identify women with early-stage cervical cancer at low risk of lymph node metastases. Eur J Cancer. 2023;185:61–8. Available from: https://www.sciencedirect.com/science /article/abs/pii/S0959804923001120.
  52. Ahsan MM, Alam TE, Haque MA, Ali MS, Rifat RH, Nafi AAN, et al. Enhancing monkeypox diagnosis and explanation through modified transfer learning, vision transformers, and federated learning. Inform Med Unlocked. 2024;45:101449. Available from: https://www.sciencedirect.com /science/article/pii/S2352914824000054.
  53. Silva S, Altmann A, Gutman B, Lorenzi M. Fed-BioMed: A general open-source frontend framework for federated learning in healthcare. Lect Notes Comput Sci. 2020;12444:201–10. Available from: https://link.springer.com/chapter/10.1007/978-3-030-60548-3_20.
  54. Upreti D, Yang E, Kim H, Seo C. A comprehensive survey on federated learning in the healthcare area: Concept and applications. CMES Comput Model Eng Sci. 2024;140(3):2239–74. Available from: https://www.researchgate.net/publication/381015354_A_Comprehensive_Survey_on_
    Federated_Learning_in_the_Healthcare_Area_Concept_and_Applications.
  55. Zhang C, Xie Y, Bai H, Yu B, Li W, Gao Y. A survey on federated learning. Knowl Based Syst. 2021;216:106775. Available from: https://www.sciencedirect.com/science/article/abs
    /pii/S0950705121000381.
  56. Zhu H, Xu J, Liu S, Jin Y. Federated learning on non-IID data: A survey. Neurocomputing. 2021;465:371–90. https://doi.org/10.1016/j.neucom.2021.07.098.
  57. Crowson MG, Moukheiber D, Arévalo AR, Lam BD, Mantena S, Rana A, et al. A systematic review of federated learning applications for biomedical data. PLOS Digit Health. 2022. https://doi.org/10.1371/journal.pdig.0000033.
  58. Asad M, Shaukat S, Hu D, Wang Z, Javanmardi E, Nakazato J, et al. Limitations and future aspects of communication costs in federated learning: A survey. Sensors. 2023;23(17):7358. Available from: https://www.mdpi.com/1424-8220/23/17/7358/htm.
  59. Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Reviewing federated learning aggregation algorithms: Strategies, contributions, limitations and future perspectives. Electronics. 2023;12(10):2287. Available from: https://www.mdpi.com/2079-9292/12/10/2287/htm.
  60. Wen J, Zhang Z, Lan Y, Cui Z, Cai J, Zhang W. A survey on federated learning: Challenges and applications. Int J Mach Learn Cybern. 2022;14(2):513–35. Available from: https://link.springer .com/article/10.1007/s13042-022-01647-y.
  61. Kholod I, Yanaki E, Fomichev D, Shalugin E, Novikova E, Filippov E, et al. Open-source federated learning frameworks for IoT: A comparative review and analysis. Sensors. 2021;21(1):167. Available from: https://www.mdpi.com/1424-8220/21/1/167/htm.
  62. Mehdi M, Makkar A, Conway M. A comprehensive review of open-source federated learning frameworks. Procedia Comput Sci. 2025;260:540–51. Available from: https://linkinghub.elsevier .com/retrieve/pii/S1877050925009767.
  63. Ziller A, Trask A, Lopardo A, Szymkow B, Wagner B, Bluemke E, et al. PySyft: A library for easy federated learning. Studies Comput Intell. 2021;965:111–39. Available from: https://link.springer .com/chapter/10.1007/978-3-030-70604-3_5.
  64. Gururaj HL, Kayarga T, Flammini F, Dobrilovic D. Federated learning techniques and their application in the healthcare industry. Federated Learning Techniques and Its Application in the Healthcare Industry. 2024.
  65. Truex S, Liu L, Chow KH, Gursoy ME, Wei W. LDP-Fed: Federated learning with local differential privacy. In: EdgeSys 2020: Proceedings of the 3rd ACM International Workshop on Edge Systems, Analytics and Networking. 2020;61–6. Available from: https://dl.acm.org/doi/pdf/10.1145
    /3378679.3394533.
  66. Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, et al. Towards federated learning at scale: System design. arXiv. 2019. Available from: http://arxiv.org/abs/1902.01046.
  67. Xiao Z, Xu X, Xing H, Song F, Wang X, Zhao B. A federated learning system with enhanced feature extraction for human activity recognition. Knowl Based Syst. 2021;229:107338. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0950705121006006.
  68. Veitch DP, Weiner MW, Miller M, Aisen PS, Ashford MA, Beckett LA, et al. The Alzheimer’s Disease Neuroimaging Initiative in the era of Alzheimer’s disease treatment: A review of ADNI studies from 2021 to 2022. Alzheimers Dement. 2024;20(1):652–94. Available from: https://doi .org/10.1002/alz.13449.
  69. Agrawal S, Sarkar S, Aouedi O, Yenduri G, Piamrat K, Alazab M, et al. Federated learning for intrusion detection system: Concepts, challenges and future directions. Comput Commun. 2022;195:346–61. https://doi.org/10.1016/j.comcom.2022.09.012.
  70. Zhou Y, Song L, Liu Y, Vijayakumar P, Gupta BB, Alhalabi W, et al. A privacy-preserving logistic regression-based diagnosis scheme for digital healthcare. Future Gener Comput Syst. 2023;144:63–73. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0167739X23000638.
  71. Pradhan K, Chawla P. Medical Internet of Things using machine learning algorithms for lung cancer detection. J Manag Anal. 2020;7(4):591–623. Available from: https://www.tandfonline .com/doi/abs/10.1080/23270012.2020.1811789.
Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 28/10/2025
Accepted 08/11/2025
Published 31/01/2026
Publication Time 95 Days
196

Login


My IP

PlumX Metrics