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Federated edge learning for medical image augmentation

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Abstract

In the medical sector, diagnostic technology-related progress is often hindered by data isolation and stringent privacy laws, posing obstacles for institutions that lack extensive disease data. This scarcity impedes the development of precise diagnostic models and reliable auxiliary tools. To address these challenges, we introduce the horizontal federated data augmentation model for medical assistance (HFDAM-MA), a novel approach designed to address the complexities of data scarcity. Our model addresses the limitations of traditional generative adversarial networks (GANs), which often rely on the independent and identically distributed (IID) assumption during training (a condition that is rarely satisfied in real-world medical data scenarios) and face computational challenges in healthcare settings. The HFDAM-MA leverages federated learning (FL) principles to enable non-IID collaborative training across multiple medical institutions. This approach alleviates the data collection pressure at individual sites and ensures the privacy of sensitive medical information. A central node orchestrates the distribution of a unified GAN model to local sites, where it operates in conjunction with two convolutional neural networks (CNNs) to generate synthetic medical images and corresponding labels. Extensive experimental results underscore the effectiveness of our model. As participation increases, we observe a substantial improvement in the diagnostic accuracy of the global model. Moreover, the performance of the local models is bolstered, and the diversity of the generated data is expanded, offering a robust solution to the challenges of data privacy, imbalanced data, and insufficient labeling that are prevalent in the medical sector.

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Data availability and access

The images supporting Fig. 7 are available for download from the work of Maftouni et al. [13] , and the dataset is divided into two categories with a total of 14,486 images, among which 7,593 positive images were collected from 466 patients and 6,893 negative images were collected from 604 patients. The MNIST dataset that supports the findings of this study is available from http://yann.lecun.com/exdb/mnist/. Data supporting this study, including both the original data generated in this research and any secondary data reused to support the results and analyses, are available from the corresponding author upon reasonable request.

References

  1. Waheed A, Goyal M, Gupta D, Khanna A, Pinheiro PR (2020) Covidgan: Data augmentation using auxiliary classifier gan for improved covid-19 detection. IEEE Access PP(99):1–1

  2. Bao H, Zhou X, Zhang Y, Li Y, Xie Y (2020) Covid-gan: Estimating human mobility responses to covid-19 pandemic through spatio-temporal conditional generative adversarial networks. In: SIGSPATIAL ’20: 28th International Conference on Advances in Geographic Information Systems

  3. Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: Challenges, methods, and future directions. IEEE Signal Proc Mag 37(3):50–60

    Article  Google Scholar 

  4. McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp 1273–1282. PMLR

  5. Li L, Fan Y, Tse M, Lin KY (2020) A review of applications in federated learning. Comput Ind Eng 149(5):106854

    Article  Google Scholar 

  6. Saaran V, Kushwaha V, Gupta S, Agarwal G (2021) A literature review on generative adversarial networks with its applications in healthcare. In: Congress on Intelligent Systems

  7. Tabassum A, Erbad A, Lebda W, Mohamed A, Guizani M (2022) Fedgan-ids: Privacy-preserving ids using gan and federated learning. Comput Commun

  8. Nguyen DC, Ding M, Pathirana PN, Seneviratne A, Zomaya AY (2022) Federated learning for covid-19 detection with generative adversarial networks in edge cloud computing. IEEE Internet Things J 9(12):10257–10271

    Article  Google Scholar 

  9. Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: Concept and applications. ACM Trans Intell Syst Technol 10(2):1–19

    Article  Google Scholar 

  10. Zhu H, Xu J, Liu S, Jin Y (2021) Federated learning on non-iid data: A survey. Neurocomputing 465:371–390

    Article  Google Scholar 

  11. Ma X, Zhu J, Lin Z, Chen S, Qin Y (2022) A state-of-the-art survey on solving non-iid data in federated learning. Futur Gener Comput Syst 135:244–258

    Article  Google Scholar 

  12. Deng L (2012) The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Proc Mag

  13. Maftouni M, Law A.C.C, Shen B, Zhou Y, Kong ZJ (2021) A robust ensemble-deep learning model for covid-19 diagnosis based on an integrated ct scan images database. In: Proceedings of the 2021 IISE Annual Conference

  14. Al-Qerem A, Alsalman YS, Mansour K (2019) Image generation using different models of generative adversarial network. In: 2019 International Arab Conference on Information Technology (ACIT), pp 241–245. IEEE

  15. Zhang H, Xu T, Li H, Zhang S, Wang X, Huang X, Metaxas DN (2019) Stackgan++: Realistic image synthesis with stacked generative adversarial networks. IEEE Comput Soc (8)

  16. Al-Qerem A, Alsalman YS, Mansour K (2019) Image generation using different models of generative adversarial network. In: 2019 International Arab Conference on Information Technology (ACIT)

  17. Ayyub A, Wadii B, Ibrahim ZA (2023) Improving satellite image classification accuracy using gan-based data augmentation and vision transformers. Earth Sci Inf 16(4):4169–4186

    Article  Google Scholar 

  18. Hardy C, Merrer EL, Sericola B (2019) Md-gan: Multi-discriminator generative adversarial networks for distributed datasets. In: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

  19. Ma X, Zhu J, Lin Z, Chen S, Qin Y (2022) A state-of-the-art survey on solving non-iid data in federated learning. Futur Gener Comput Syst 135:244–258

    Article  Google Scholar 

  20. Oh S, Park J, Jeong E, Kim H, Kim SL (2020) Mix2fld: Downlink federated learning after uplink federated distillation with two-way mixup. IEEE Commun Lett PP(99):1–1

  21. Li Z, Shao J, Mao Y, Wang JH, Zhang J (2022) Federated learning with gan-based data synthesis for non-iid clients. In: International Workshop on Trustworthy Federated Learning, pp 17–32. Springer

  22. Cao X, Sun G, Guizani YM (2023) Perfed-gan: Personalized federated learning via generative adversarial networks. IEEE Internet Things J 10(5):3749–3762

    Article  Google Scholar 

  23. Zhang L, Shen B, Barnawi A, Xi S, Kumar N, Wu Y (2021) Feddpgan: Federated differentially private generative adversarial networks framework for the detection of covid-19 pneumonia. Inf Syst Front

  24. Sattler F, Wiedemann S, Müller KR, Samek W (2019) Robust and communication-efficient federated learning from non-iid data. IEEE Trans Neural Netw Learn Syst 31(9):3400–3413

    Article  Google Scholar 

  25. Mothukuri V, Parizi RM, Pouriyeh S, Huang Y, Dehghantanha A, Srivastava G (2021) A survey on security and privacy of federated learning. Futur Gener Comput Syst FGCS (115-):115

  26. Rodriguez-Barroso N, Jimenez-Lopez D, Victoria Luzon M, Herrera F, Martinez-Camara E (2023) Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges. Inf Fusion

  27. Shen S, Zhu T, Wu D, Wang W, Zhou W (2020) From distributed machine learning to federated learning: In the view of data privacy and security. Concurr Comput Pract Experience (2)

  28. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139–144

    Article  MathSciNet  Google Scholar 

  29. Kingma D, Ba J (2014) Adam: A method for stochastic optimization. Comput Sci

  30. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv Neural Inf Proc Syst 30

  31. Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, Jin S, Quek TQ, Poor HV (2020) Federated learning with differential privacy: Algorithms and performance analysis. IEEE Trans Inf Forensic Secur 15:3454–3469

    Article  Google Scholar 

  32. Truex S, Liu L, Chow KH, Gursoy ME, Wei W (2020) Ldp-fed: Federated learning with local differential privacy. In: Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking, pp 61–66

  33. Pei J, Yu Z, Li J, Jan MA, Lakshmanna K (2023) Tkagfl: A federated communication framework under data heterogeneity. IEEE Trans Netw Sci Eng 5:10

    Google Scholar 

  34. Połap D, Srivastava G, Yu K (2021) Agent architecture of an intelligent medical system based on federated learning and blockchain technology. J Inf Secur Appl 58:102748

  35. Hatua A, Nguyen TT, Sung AH (2019) Dialogue generation using self-attention generative adversarial network. In: 2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE)

  36. Zhang Z, Li M, Yu J (2019) D2pggan: Two discriminators used in progressive growing of gans. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Funding

This work is funded by: The talent project of Department of Science and Technology of Jilin Province of China under Grant No.20240602106RC, and by the Central University Basic Scientific Research Fund under Grant No.2023-JCXK-04,and by Key scientific and technological R&D Plan of Jilin Province of China under Grant No. 20230201066GX, and by Project of Jilin Province Development and Reform Commission, China under Grant No. 2019FGWTZC001.

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Author notes

  1. Juncheng Hu and Hongtu Li are authors that contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, 130000, Qianjin Street, Changchun, China

    Shuai Li, Liang Hu, Chengyu Sun, Juncheng Hu & Hongtu Li

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  1. Shuai Li
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  2. Liang Hu
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Contributions

Shuai Li is responsible for integrating and writing the article, Chengyu Sun is responsible for providing the data, Juncheng Hu is responsible for writing the manuscript, Professor Hongtu Li is responsible for providing writing guidance for the article, and Liang Hu is responsible for proofreading the article.

Corresponding authors

Correspondence to Juncheng Hu or Hongtu Li.

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Competing interests

We the undersigned declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. We declare that we have no conflicts of interest related to this work. We declare that we do not have any commercial or associative interests that represent conflicts of interest in connection with the work submitted.

Ethical and informed consent for data used

This study does not involve human or animal subjects and does not utilize sensitive data. Our research is based on an analysis of publicly available datasets, which have been deidentified to ensure the protection of individual privacy. We confirm that all the data used in this study comply with the applicable data protection regulations and privacy policies.

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Li, S., Hu, L., Sun, C. et al. Federated edge learning for medical image augmentation. Appl Intell 55, 56 (2025). https://doi.org/10.1007/s10489-024-06046-0

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