A Federated Autoencoder Framework With Explainable AI for Intelligent 6G-IoT Infrastructure Optimization

Sixth Generation (6G) wireless networks with ultra-low latency, high reliability, and massive connectivity require intelligent and privacy-concerned infrastructure optimization. This work presents a federated autoencoder platform combined with Explainable AI (XAI) for performance optimization of 6G-IoT systems. The method integrates traditional machine learning algorithms (Decision Tree, Random Forest, Logistic Regression, AdaBoost, Gradient Boosting) with a Variational Autoencoder (VAE) for dimensionality reduction and feature extraction. Federated Learning (FL) is utilized to maintain data privacy among distributed edge nodes, and SHAP and LIME explainers are utilized for explaining model decisions at the local and global levels. The framework points out key QoS parameters like latency and throughput as major optimization levers. Experimental outcomes on the 6G-IoT dataset indicate that Random Forest with highest accuracy for 80:20 split and Gradient Boosting has a 99.8% accuracy in a 10-fold validation, and FL gets a ROC-AUC value of 0.999 with robust privacy guarantees. XAI enhances transparency and regulatory compliance by making attribution of predictions to contributing features. As a whole, the proposed approach provides an interpretable, privacy-conscientious, and scalable tool for intelligent 6G-IoT infrastructure management.