Hierarchical Federated Agentic Intelligence for Privacy-Preserving Analytics and Autonomous Management in Advanced Network Infrastructures
DOI:
https://doi.org/10.70589/JRTCSE.2023.2.12Keywords:
Hierarchical Federated Learning, Agentic Artificial Intelligence, Large Language Models (LLMs), Network Data Analytics Function (NWDAF), Privacy-Preserving Learning, Retrieval-Augmented Generation (RAG), Knowledge Graphs, Explainable Artificial Intelligence (XAI), Network Slicing, Autonomous Network Management, Edge Intelligence, 6G NetworksAbstract
The rapid evolution of intelligent communication infrastructures, including 5G, beyond 5G (B5G), 6G, edge-cloud computing, software-defined networking (SDN), and network function virtualization (NFV), has significantly increased the demand for scalable, privacy-preserving, and autonomous network intelligence. Although Federated Learning (FL) enables collaborative model training without exposing raw network data, conventional FL frameworks remain constrained by communication overhead, limited semantic reasoning capabilities, insufficient explainability, and vulnerability to privacy leakage and heterogeneous network conditions. This paper proposes a Hierarchical Federated Agentic Intelligence (HFAI) framework that integrates hierarchical federated learning, Large Language Model (LLM)-driven collaborative agents, knowledge graphs, Retrieval-Augmented Generation (RAG), and explainable artificial intelligence to enable intelligent, secure, and context-aware network analytics. The proposed architecture employs specialized autonomous agents responsible for traffic prediction, anomaly detection, network slicing optimization, radio resource allocation, service orchestration, fault diagnosis, and incident response, all coordinated through a hierarchical analytics framework inspired by the Network Data Analytics Function (NWDAF) architecture. Furthermore, an adaptive privacy-preserving aggregation mechanism enables secure collaborative learning across distributed network entities while protecting sensitive operational information and minimizing communication overhead. Knowledge graph reasoning and retrieval-augmented intelligence significantly improve contextual understanding, decision transparency, and explainability of network management operations. The proposed HFAI framework provides a unified foundation for autonomous network optimization, privacy-preserving collaborative intelligence, and trustworthy AI-driven decision-making, making it well suited for next-generation AI-native communication networks.
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Copyright (c) 2026 Bala Yashwanth Reddy Thumma, Abhignan Srivatsava Sribhashyam, Supraja Ayyamgari, Charan Thumma (Author)
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