PAF-DMAO: A Privacy-Aware Federated Diffusion Framework for Multi-Agent Resource Orchestration in 6G NTN-V2X Networks

The convergence of non-terrestrial networks (NTNs) and vehicle-to-everything (V2X) communications is emerging as a key enabler of AI-native 6G intelligent transportation systems, providing seamless connectivity across urban, rural, and remote environments through an integrated space-air-ground architecture. However, efficiently orchestrating communication, computation, and energy resources remains challenging due to high mobility, network heterogeneity, stringent latency requirements, and privacy concerns. To address these issues, we propose PAF-DMAO, a privacy-aware federated diffusion framework that integrates diffusion-based optimization, federated learning, distributed multi-agent coordination, differential privacy, and Alternating Direction Method of Multipliers (ADMM)-based consensus for intelligent NTN-V2X resource orchestration without sharing sensitive vehicular data. Extensive simulations demonstrate that PAF-DMAO achieves performance comparable to centralized optimization while significantly improving throughput, energy efficiency, scalability, and privacy-utility trade-offs over state-of-the-art baselines. Aligned with AI-native networking and ongoing 3GPP and ETSI standardization efforts.