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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64401

FEDERATED BIG DATA PLATFORM FOR GLOBAL HEALTH RESEARCH

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64401

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

GMH

Gagan Mrutyunjaya Hiremath

MASTERS OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE

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SMT

SHRUTHI M T

MASTERS OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE

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Abstract

The global health landscape is characterized by an exponential growth of data, offering unprecedented opportunities for research and advancements in healthcare. However, this data is often. fragmented across various institutions, siloed by geographical and administrative boundaries, and subject to stringent privacy regulations. This fragmentation. severely hampers large-scale, collaborative research efforts that are crucial for tackling global health challenges. This project. proposes the development of a Federated Big Data Platform specifically designed for global. health research. This platform. will leverage the principles of federated learning, a groundbreaking machine learning approach that allows for the collaborative. training of models on decentralized data without the need to move sensitive patient. information. The core of this platform is a decentralized architecture where data remains at its source institution, ensuring data privacy, security, and compliance with regulations. such as HIPAA and GDPR. By bringing the analysis to the data, the platform will enable researchers. from around the world to collaborate on building robust and generalizable models from. diverse datasets. This approach not only protects patient confidentiality but also fosters trust and encourages participation. from a wider range of healthcare organizations. The platform. will incorporate and secure multi-party computation, to further safeguard data integrity. Its key features will include. .a user-friendly interface for researchers to design and execute federated queries, tools. for data harmonization to address the heterogeneity. of healthcare data, and a robust governance framework to manage data access and usage. The proposed system. aims existing centralized data-sharing. models, which often face legal, ethical, and logistical barriers.

KEYWORDS

Big Data Platform

Cite This Article

Gagan Mrutyunjaya Hiremath, SHRUTHI M T, "FEDERATED BIG DATA PLATFORM FOR GLOBAL HEALTH RESEARCH," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026.

Full Article/Conclusion

The "Federated Big Data Platform for Global Health Research".  project presents a transformative solution to the long-standing challenges of data fragmentation, privacy, and.  security in the field of global health. By embracing the principles of federated learning and a decentralized architecture, the platform offers a novel and powerful.  approach to collaborative research, enabling scientists from around the world to unlock the immense.  potential of big data in a secure and ethical manner. the limitations.  of existing centralized data models by allowing data to remain at its source, thereby preserving.  patient privacy, ensuring data sovereignty, and fostering trust among participating institutions. The platform's robust.  security features, comprehensive governance framework, and user-friendly interface will create a sustainable and scalable ecosystem for.  data-driven discovery.   The successful implementation of this platform will have a profound.  and far-reaching impact on global health. It will accelerate the pace of research in critical areas such.  as disease surveillance, precision medicine, and drug discovery. By providing access to larger and more.  diverse datasets, the platform will enable the development of more accurate and generalizable predictive models, leading.  to patient outcomes. Furthermore, the platform will democratize access to data and analytical tools, empowering a wider range of researchers and institutions to participate in cutting-edge research. This will be particularly.  beneficial for researchers in low- and middle-income countries, who have historically been underrepresented in.  global health research.   In conclusion, the Federated Big Data Platform for Global Health.  Research is not merely a technological innovation; it is a catalyst for a new era of open, collaborative, and impactful science. By breaking down the silos that have.  traditionally hindered progress, the platform.  will foster a global community of researchers who are united in their commitment to improving the.  health and well-being of people everywhere. The successful realization of this project will be a significant.  step forward in our collective journey.  towards a healthier and more equitable world

References

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Google AI.Sheller, M. J., Edwards, B., Reina, G. A., et al. (2020). Federated learning in medicine: Enabling multi-institutional collaborations. Nature Scientific Reports.Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine.Teo, J. W., Tan, J., & Lim, S. (2024). Systematic review of federated learning applications in healthcare. PMC.Zhao, R., Li, X., & Wang, Y. (2023). Explainable federated learning for smart healthcare. PubMed.Eden, H., et al. (2025). Governance mechanisms for federated learning in healthcare. Nature Digital Medicine.Madathil, K. C., et al. (2024). Revolutionizing healthcare data analytics with federated learning. ScienceDirect.Zhang, Y., et al. (2023). Recent methodological advances in federated learning for healthcare. ScienceDirect.Pati, S., et al. (2024). Privacy preservation techniques for federated learning in healthcare. ScienceDirect.Abbas, S., et al. (2025). Federated learning applications in smart healthcare systems. PubMed. 

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Publication Date

6/1/2026

Peer Review Type

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Review Rounds: 2

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