Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference

2025/10/09 by Yannis Belkhiter, Seshu Tirupathi, Belkhiter, Yannis +7
Computer Science · Engineering · #Advanced Data and IoT Technologies #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Traffic Prediction and Management Techniques

paper · pdf · doi:10.48550/arxiv.2510.08303

openalex publication_date 2025/10/09 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28

Abstract

As AI becomes a native component of 6G network control, AI models must adapt to continuously changing conditions, including the introduction of new features and measurements driven by multi-vendor deployments, hardware upgrades, and evolving service requirements. To address this growing need for flexible learning in non-stationary environments, this vision paper highlights Adaptive Random Forests (ARFs) as a reliable solution for dynamic feature adaptation in communication network scenarios. We show that iterative training of ARFs can effectively lead to stable predictions, with accuracy improving over time as more features are added. In addition, we highlight the importance of explainability in AI-driven networks, proposing Drift-Aware Feature Importance (DAFI) as an efficient XAI feature importance (FI) method. DAFI uses a distributional drift detector to signal when to apply computationally intensive FI methods instead of lighter alternatives. Our tests on 3 different datasets indicate that our approach reduces runtime by up to 2 times, while producing more consistent feature importance values. Together, ARFs and DAFI provide a promising framework to build flexible AI methods adapted to 6G network use-cases.

Citations

• Reliable Vertical Federated Learning in 5G Core Network Architecture
• XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection
• Decentralized Multi-Party Multi-Network AI for Global Deployment of 6G Wireless Systems
• River: machine learning for streaming data in Python
• Trees, forests, and impurity-based variable importance
• A Unified Approach to Interpreting Model Predictions

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