Privacy-Preserving Federated Learning for Pneumonia Diagnosis

Sagnik Sarkar; Shaashwat Agrawal; Thippa Reddy Gadekallu; Mufti Mahmud; David J. Brown

doi:10.1007/978-981-99-1648-1_29

Privacy-Preserving Federated Learning for Pneumonia Diagnosis

Sagnik Sarkar
, Shaashwat Agrawal
, Thippa Reddy Gadekallu
, Mufti Mahmud^*
, David J. Brown

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

Early diagnosis of diseases has become the major focus of researchers today. Machine Learning (ML) and Deep Learning (DL) algorithms have provided a much-needed boost to this field to make early diagnosis possible. With the help of sufficient data and computation, DL algorithms can be developed to detect diseases with high precision. Even with this uplift, gathering high-quality and quantity data for medical studies is very difficult. Although implementing Federated Learning (FL) solves data availability issues, its high variance makes it incompatible with Medical Diagnosis. In this paper, we present an FL approach for diagnosing pneumonia in patients that is easy to use and provides state-of-the-art results. Before training, a combination of pre-processing steps is performed that significantly increase the performance of the FL architecture.

Original language	English
Title of host publication	Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings
Editors	Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatowt
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	345-356
Number of pages	12
ISBN (Print)	9789819916474
DOIs	https://doi.org/10.1007/978-981-99-1648-1_29
State	Published - 2023
Externally published	Yes
Event	29th International Conference on Neural Information Processing, ICONIP 2022 - Virtual, Online Duration: 22 Nov 2022 → 26 Nov 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1794 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	29th International Conference on Neural Information Processing, ICONIP 2022
City	Virtual, Online
Period	22/11/22 → 26/11/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Federated Learning
Image Processing
Medical Diagnosis

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-981-99-1648-1_29

Cite this

Sarkar, S., Agrawal, S., Gadekallu, T. R., Mahmud, M., & Brown, D. J. (2023). Privacy-Preserving Federated Learning for Pneumonia Diagnosis. In M. Tanveer, S. Agarwal, S. Ozawa, A. Ekbal, & A. Jatowt (Eds.), Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings (pp. 345-356). (Communications in Computer and Information Science; Vol. 1794 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-1648-1_29

Sarkar, Sagnik ; Agrawal, Shaashwat ; Gadekallu, Thippa Reddy et al. / Privacy-Preserving Federated Learning for Pneumonia Diagnosis. Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings. editor / Mohammad Tanveer ; Sonali Agarwal ; Seiichi Ozawa ; Asif Ekbal ; Adam Jatowt. Springer Science and Business Media Deutschland GmbH, 2023. pp. 345-356 (Communications in Computer and Information Science).

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abstract = "Early diagnosis of diseases has become the major focus of researchers today. Machine Learning (ML) and Deep Learning (DL) algorithms have provided a much-needed boost to this field to make early diagnosis possible. With the help of sufficient data and computation, DL algorithms can be developed to detect diseases with high precision. Even with this uplift, gathering high-quality and quantity data for medical studies is very difficult. Although implementing Federated Learning (FL) solves data availability issues, its high variance makes it incompatible with Medical Diagnosis. In this paper, we present an FL approach for diagnosing pneumonia in patients that is easy to use and provides state-of-the-art results. Before training, a combination of pre-processing steps is performed that significantly increase the performance of the FL architecture.",

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Sarkar, S, Agrawal, S, Gadekallu, TR, Mahmud, M & Brown, DJ 2023, Privacy-Preserving Federated Learning for Pneumonia Diagnosis. in M Tanveer, S Agarwal, S Ozawa, A Ekbal & A Jatowt (eds), Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings. Communications in Computer and Information Science, vol. 1794 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 345-356, 29th International Conference on Neural Information Processing, ICONIP 2022, Virtual, Online, 22/11/22. https://doi.org/10.1007/978-981-99-1648-1_29

Privacy-Preserving Federated Learning for Pneumonia Diagnosis. / Sarkar, Sagnik; Agrawal, Shaashwat; Gadekallu, Thippa Reddy et al.
Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings. ed. / Mohammad Tanveer; Sonali Agarwal; Seiichi Ozawa; Asif Ekbal; Adam Jatowt. Springer Science and Business Media Deutschland GmbH, 2023. p. 345-356 (Communications in Computer and Information Science; Vol. 1794 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Early diagnosis of diseases has become the major focus of researchers today. Machine Learning (ML) and Deep Learning (DL) algorithms have provided a much-needed boost to this field to make early diagnosis possible. With the help of sufficient data and computation, DL algorithms can be developed to detect diseases with high precision. Even with this uplift, gathering high-quality and quantity data for medical studies is very difficult. Although implementing Federated Learning (FL) solves data availability issues, its high variance makes it incompatible with Medical Diagnosis. In this paper, we present an FL approach for diagnosing pneumonia in patients that is easy to use and provides state-of-the-art results. Before training, a combination of pre-processing steps is performed that significantly increase the performance of the FL architecture.

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Sarkar S, Agrawal S, Gadekallu TR, Mahmud M, Brown DJ. Privacy-Preserving Federated Learning for Pneumonia Diagnosis. In Tanveer M, Agarwal S, Ozawa S, Ekbal A, Jatowt A, editors, Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 345-356. (Communications in Computer and Information Science). doi: 10.1007/978-981-99-1648-1_29