Federated Learning for Privacy-Preserving HR Analytics in Healthcare and Finance

Authors

  • Sudheer Devaraju  Walmart Global Tech, Banglore, India
  • Srikanth Katta  Takeda Global, Haryana, India

DOI:

https://doi.org/10.32628/IJSRSET23116180

Keywords:

Federated Learning, HR Analytics, Data Privacy, Healthcare, Finance

Abstract

HR analytics and data privacy are becoming more important, especially in high regulation industries like healthcare and finance, and AI is being used in these analytics more and more. However, centralized machine learning approaches are still traditionally based on centralizing sensitive employee data across various companies, breaking privacy rules, and enhancing security threats. In this paper, we discuss how federated learning can be a new paradigm of collaborative training of AI models across organizations without breaking data privacy. In this work, we leverage a federated learning framework to enable healthcare and finance companies to jointly train HR analytics models with data remaining locally under constraints of privacy regulations. The framework protects individual employee data in the collaborative learning process, through secure aggregation protocols, differential privacy techniques and homomorphic encryption. We evaluate the framework on real world datasets and demonstrate how the framework improves model performance and privacy preservation. We demonstrate in our federated learning results that we can achieve similar accuracy as centralized training with greatly reduced privacy risk. This research demonstrates the potential of federated learning in privacy preserving HR analytics and cross organizational collaboration in sensitive industries.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-11-16

Issue

Volume 10, Issue 6, November-December-2023

Section

Research Articles

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How to Cite

[1]
Sudheer Devaraju, Srikanth Katta "Federated Learning for Privacy-Preserving HR Analytics in Healthcare and Finance" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 6, pp.415-423, November-December-2023. Available at doi : https://doi.org/10.32628/IJSRSET23116180