Enhancing network science with large language models and federated artificial intelligence

Abstract

Abstract Sixth generation (6G) networks require intelligent and privacy-preserving solutions to handle traffic complexity, resource allocation, and security challenges. While Large Language Models (LLMs) offer advanced reasoning and Federated Artificial Intelligence (FAI) enables distributed learning without directly sharing raw data, their combined application in large-scale network science remains limited. Key issues include non-IID data, decision latency, and limited robustness of centralized models. To address these challenges, this work introduces FALCON-6G (Federated AI and LLM-Oriented Cognitive Optimization in 6G Networks), a novel framework for distributed anomaly detection, traffic prediction, and adaptive resource management. Experiments conducted under federated learning settings with 50 participating clients and 200 global communication rounds on two Open RAN and four intrusion detection datasets show a 22.7% average relative improvement in throughput prediction accuracy and a 17.6% reduction in decision latency compared with FedAvg, along with up to a 23.9% relative improvement in intrusion detection F1-score over FedAvg and a 34.0% reduction in communication overhead. These results demonstrate the potential of FALCON-6G for privacy-aware 6G network optimization. The current evaluation is primarily based on benchmark datasets, and validation in live large-scale 6G/O-RAN environments remains an important direction for future work.