Federated Learning Framework for Privacy-Preserving Voice Biometrics in Multi-Tenant Contact Centers

Author(s)	Siva Venkatesh Arcot
Country	United States
Abstract	This paper presents a novel federated learning framework for implementing voice biometric authentication in multi-tenant cloud contact centers while preserving customer privacy and regulatory compliance. Traditional centralized voice biometric systems face significant challenges in cloud environments due to data sovereignty requirements, privacy regulations (GDPR, CCPA), and tenant isolation needs. Our proposed framework enables distributed learning across tenant boundaries without exposing raw voice data, achieving 97.3% authentication accuracy while maintaining strict data locality. The system leverages differential privacy mechanisms and homomorphic encryption to protect individual voice patterns during model aggregation. Implementation results from a Cisco Webex Contact Center environment serving 50+ enterprise tenants demonstrate 40% reduction in authentication latency and 99.8% compliance with data residency requirements. This approach addresses critical gaps in secure biometric deployment for cloud-native contact centers while enabling cross-tenant learning benefits.
Keywords	Federated learning, voice biometrics, privacy preservation, multi-tenant architecture, contact centers, differential privacy, homomorphic encryption
Field	Engineering
Published In	Volume 4, Issue 5, September-October 2022
Published On	2022-10-10

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International Journal For Multidisciplinary Research