FEDERATED KNOWLEDGE DISTILLATION FRAMEWORK FOR PRIVACY-PRESERVING EDGE AI IN LOW-BANDWIDTH RURAL NETWORKS OF INDIA EMPOWERING INCLUSIVE DIGITAL TRANSFORMATION THROUGH LIGHTWEIGHT MULTI-MODAL INTELLIGENCE AND CONTEXT-AWARE SYNCHRONIZATION

This paper introduces a novel Federated Knowledge Distillation (Fed-KD) framework designed to address the dual challenges of privacy preservation and bandwidth constraints in rural Indian networks. By utilizing lightweight multi-modal intelligence and context-aware synchronization, the model ensures efficient learning across distributed edge devices without transmitting raw data. The approach achieves superior performance over traditional Federated Learning (FL) methods by minimizing communication overhead and improving generalization through collaborative distillation. This paradigm fosters inclusive digital transformation, enabling equitable AI access and fostering socio-economic upliftment in underserved regions.