Explainable and Trust-Aware AI-Driven Network Slicing Framework for 6G IoT Using Deep Learning

The advent of sixth-generation (6G) networks promises ultra-high bandwidth, reliable connectivity, and ultra-low latency, enabling large-scale IoT deployment. Network slicing is central to these capabilities, but conventional deep learning approaches often suffer from privacy risks, high computational cost, and poor energy efficiency. To address these challenges, this work proposes a federated and explainable AI framework for energy-efficient IoT slicing in 6G. Federated learning enables collaborative training without sharing raw data, preserving privacy and reducing communication overhead. A transformer-based model captures complex traffic patterns, while a hybrid swarm-intelligence optimizer balances throughput, latency, and energy consumption. SHAP-based explainability enhances transparency in slice allocation. Experiments on real and simulated traffic confirm superior performance, with the proposed framework achieving 98.42% accuracy compared to CNN+LSTM (92.67%) and HHO-CNN+LSTM (95.12%). These results demonstrate a scalable, privacy-preserving, and sustainable solution for future 6G IoT ecosystems.