Open access
Aug 2026
Investigating Privacy-Preserving Federated Learning for Telecom Customer Churn Prediction Using Differential Privacy
This paper investigates a privacy-preserving approach to churn prediction that combines federated learning (FL) with differential privacy (DP), and highlights the potential of privacy-preserving federated learning for practical distributed analytics applications where protecting sensitive data is essential.
Alisha Sikri, Shalini Gambhir, Roshan Jameel et al.
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