Manuscript Number : IJSRSET23116180
Federated Learning for Privacy-Preserving HR Analytics in Healthcare and Finance
Authors(2) :-Sudheer Devaraju, Srikanth Katta
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.
Sudheer Devaraju
Federated Learning, HR Analytics, Data Privacy, Healthcare, Finance
Publication Details
Published in :
Volume 10 | Issue 6 | November-December 2023 Article Preview
Walmart Global Tech, Banglore, India
Srikanth Katta
Takeda Global, Haryana, India
Date of Publication :
2023-11-16
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) :
415-423
Manuscript Number :
IJSRSET23116180
Publisher : Technoscience Academy
Journal URL :
https://res.ijsrset.com/IJSRSET23116180