Federated Learning for Privacy-Preserving Employee Performance Analytics

Federated Learning for Privacy-Preserving Employee Performance Analytics

With the increasing sensitivity surrounding employee performance data, there is a pressing need for predictive systems that preserve privacy while delivering actionable insights to organizations. This paper introduces HFAN-Priv, a hierarchical federated attention network designed to predict employee...

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Bibliographic Details
Main Author: Jay Barach
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Federated learning
employee performance
privacy-preserving AI
differential privacy
SHAP
human resource analytics
Online Access:https://ieeexplore.ieee.org/document/11087544/
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https://ieeexplore.ieee.org/document/11087544/

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