Federated Learning Applications for Cross Cultural Communication Analysis Through Privacy Preserving Integration of Linguistic and Paralinguistic Feature Extraction

Abstract

Analysis of communication across cultures is essential in global digital interactions, requiring models that capture linguistic and paralinguistic features across diverse cultures. However, existing studies lack privacy-preserving frameworks that can effectively model both linguistic and paralinguistic features across distributed and culturally diverse datasets, highlighting a critical research gap. Existing centralized approaches pose privacy risks and often fail to preserve cultural nuances in distributed communication data. This research presents a CAFL to integrate linguistic and paralinguistic features across multiple cultural groups while maintaining data decentralization. A culture-specific gradient aggregation strategy is introduced to reduce representation bias and improve performance in low-resource languages. Evaluation across 27 cultural groups shows 93.7% accuracy in communication pattern recognition and an 87.4% reduction in privacy risk compared to centralized methods. The model captures prosodic variations, conversational timing, and non-verbal cues often overlooked by conventional NLP techniques. The findings reveal new cross-cultural communication patterns and provide a scalable, privacy-aware solution for intercultural collaboration, global communication systems, and culturally adaptive AI.