Skip to main content
Log in

Collaborative, Privacy-Preserving Federated Learning Framework for the Detection of Diabetic Eye Diseases

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Diabetes Mellitus is one of the chronic metabolic disorders that is easily identified by elevation in blood sugar levels. The high blood sugar levels over time can lead to substantial damage to the blood vessels, heart, eyes, kidneys, and nerves. Diabetic Eye Diseases (DED) such as Diabetic Macular Edema (DME), Diabetic Retinopathy (DR), and glaucoma, are serious complications of diabetes that can result in irreversible vision loss. Thus, affecting the health and quality of life of the population worldwide. The primary objective of the proposed work is to evaluate the efficacy of a privacy-preserved Federated Learning (FL) framework for detecting DEDs. To detect DEDs a collaborative and privacy-preserving Federated Deep Learning (FDL) framework using a lightweight MobileNetV2 architecture is proposed. The aggregation scheme selected for the FDL framework is FedProx and to achieve a high accuracy score the proximal term, µ is tuned with values ranging from µ= (0.01, 0.02, 0.03, 0.04 and 0.05). The impact of the variance in the proximal term on model performance is analyzed, and the results show that higher µ (0.03,0.4 and 0.5) values provide more stable and consistently high precision, recall, and F1-scores. The findings suggest that FedProx aggregation successfully stabilizes the performance of the framework with the least standard deviation of 0.035 in accuracy at µ = 0.05. The balance between accuracy and stability is a key factor in the successful application of federated learning in disease prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

The dataset used in this research is available at the following repositories. https://github.com/Traslational-Visual-Health-Laboratory/OCT-AND-EYE-FUNDUS-DATASET. https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid. https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k. https://www5.cs.fau.de/research/data/fundus-images/.

References

  1. Diabetes. Accessed: Jun. 28, 2024. [Online]. Available: https://www.who.int/health-topics/diabetes/diabetes#tab=tab_1

  2. Diabetic Retinopathy - Middle East - American Academy of Ophthalmology. Accessed: Jun. 28, 2024. [Online]. Available: https://www.aao.org/education/topic-detail/diabetic-retinopathy-middle-east

  3. Promoting Eye Health | Diabetes | CDC. Accessed: Jun. 28. 2024. [Online]. Available: https://www.cdc.gov/diabetes/hcp/clinical-guidance/promote-eye-health.html

  4. Gulati S, Guleria K, Goyal N. Classification and Detection of Coronary Heart Disease using Machine Learning, in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 1728–1732. https://doi.org/10.1109/ICACITE53722.2022.9823547

  5. Gulati S, Guleria K, Goyal N. Classification of Migraine Disease using Supervised Machine Learning, in 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2022, pp. 1–7.

  6. Jain R, Kukreja V, Chattopadhyay S, Verma A, Sharma R. Radial Basis Function Integrated with Support Vector Machine Model for Breast Cancer Detection, in 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), 2024, pp. 1–5. https://doi.org/10.1109/AIMLA59606.2024.10531382

  7. Gulati S, Guleria K, Goyal N. Classification of Diabetic Retinopathy using pre-trained Deep Learning Model- DenseNet 121, in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2023, pp. 1–6. https://doi.org/10.1109/ICCCNT56998.2023.10308181

  8. Gulati S, Guleria K, Goyal N. Classification and detection of diabetic eye diseases using deep learning: A review and comparative analysis, AIP Conf Proc, vol. 2916, no. 1, p. 020005, Dec. 2023, https://doi.org/10.1063/5.0177682

  9. Sharma S, Guleria K. A comprehensive review on federated learning based models for healthcare applications. Artif Intell Med. 2023;146:102691. https://doi.org/10.1016/j.artmed.2023.102691.

    Article  Google Scholar 

  10. Liu J, et al. From distributed machine learning to federated learning: a survey. Knowl Inf Syst. 2022;64(4):885–917. https://doi.org/10.1007/s10115-022-01664-x.

    Article  Google Scholar 

  11. Li T, Sahu AK, Talwalkar A, Smith V. Federated Learning: challenges, methods, and future directions. IEEE Signal Process Mag. 2020;37(3):50–60. https://doi.org/10.1109/MSP.2020.2975749.

    Article  Google Scholar 

  12. Kaur H, Rani V, Kumar M, Sachdeva M, Mittal A, Kumar K. Federated learning: a comprehensive review of recent advances and applications. Multimedia Tools Appl. 2023. https://doi.org/10.1007/s11042-023-17737-0.

    Article  Google Scholar 

  13. Zhang H, Bosch J, Olsson HH. Federated learning systems: Architecture alternatives, in 2020 27th Asia-Pacific Software Engineering Conference (APSEC), 2020, pp. 385–394.

  14. Zhang C, Xie Y, Bai H, Yu B, Li W, Gao Y. A survey on federated learning. Knowl Based Syst. Mar. 2021;216:106775. https://doi.org/10.1016/J.KNOSYS.2021.106775.

  15. Li L, Fan Y, Tse M, Lin K-Y. A review of applications in federated learning. Comput Ind Eng. 2020;149:106854. https://doi.org/10.1016/j.cie.2020.106854.

    Article  Google Scholar 

  16. Lepcha DC, Dogra A, Goyal B, Goyal V, Kukreja V, Bavirisetti DP. A constructive non-local means algorithm for low-dose computed tomography denoising with morphological residual processing. PLoS ONE. 2023;18(9):e0291911.

    Article  Google Scholar 

  17. Dogra A, Goyal B, Agrawal S. Osseous and digital subtraction angiography image fusion via various enhancement schemes and laplacian pyramid transformations. Future Generation Comput Syst. 2018;82:149–57.

    Article  Google Scholar 

  18. Qi P, Chiaro D, Guzzo A, Ianni M, Fortino G, Piccialli F. Model aggregation techniques in federated learning: a comprehensive survey. Future Generation Comput Syst, 2023.

  19. Pillutla K, Kakade SM, Harchaoui Z. Robust aggregation for Federated Learning. IEEE Trans Signal Process. 2022;70:1142–54. https://doi.org/10.1109/TSP.2022.3153135.

    Article  MathSciNet  Google Scholar 

  20. Hanif A, et al. Federated Learning for Multicenter collaboration in Ophthalmology: implications for clinical diagnosis and Disease Epidemiology. Ophthalmol Retina. 2022;6(8):650–6. https://doi.org/10.1016/j.oret.2022.03.005.

    Article  Google Scholar 

  21. Baptista T, Soares C, Oliveira T, Soares F. Federated Learning for computer-aided diagnosis of Glaucoma using Retinal Fundus images, Applied Sciences, 13, 21, 2023, https://doi.org/10.3390/app132111620

  22. Chetoui M, Akhloufi MA. Federated Learning for Diabetic Retinopathy Detection Using Vision Transformers. BioMedInformatics. 2023;3(4):948–61. https://doi.org/10.3390/biomedinformatics3040058.

    Article  Google Scholar 

  23. Tang Z, Wong H-S, Yu Z. Privacy-Preserving Federated Learning with Domain Adaptation for Multi-disease Ocular Disease Recognition. IEEE J Biomedical Health Inf. 2023;1–9. https://doi.org/10.1109/JBHI.2023.3305685.

  24. Mohan NJ, Murugan R, Goel T, Roy P. DRFL: Federated Learning in Diabetic Retinopathy Grading Using Fundus Images, IEEE Transactions on Parallel and Distributed Systems, 2023.

  25. Aljohani A, Aburasain RY. A hybrid framework for glaucoma detection through federated machine learning and deep learning models. BMC Med Inf Decis Mak. 2024;24(1):115.

    Article  Google Scholar 

  26. Translational Visual Health Laboratory. OCT and Eye Fundus Dataset. Available: https://github.com/Traslational-Visual-Health-Laboratory/OCT-AND-EYE-FUNDUS-DATASET

  27. Porwal P et al. Indian Diabetic Retinopathy Image Dataset (IDRiD). IEEE Dataport, 2018. doi: 10.21227/H25W98. Available: https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid

  28. Ltd. Shanggong Medical Technology Co. Ocular Disease Intelligent Recognition (ODIR) Dataset. Available: https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k

  29. Budai A, Bock R, Maier A, Hornegger J, Michelson G. Robust Vessel Segmentation in Fundus Images, Int J Biomed Imaging, vol. 2013, 2013, [Online]. Available: https://www5.cs.fau.de/research/data/fundus-images/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kalpna Guleria.

Ethics declarations

Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gulati, S., Guleria, K. & Goyal, N. Collaborative, Privacy-Preserving Federated Learning Framework for the Detection of Diabetic Eye Diseases. SN COMPUT. SCI. 5, 1100 (2024). https://doi.org/10.1007/s42979-024-03462-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-03462-4

Keywords

Navigation