Federated Learning-Empowered Jamming Detection for Stochastic Uplink and Downlink Integrated Terrestrial and Non-Terrestrial Networks

In this paper, we present a novel approach that integrates the reliability and fast data transfer capabilities of terrestrial-based infrastructure with the extensive coverage of non-terrestrial networks, thereby creating a seamless and resilient communication network. The proposed integration addresses the issue of adaptive jamming in uplink (UL) and downlink (DL) transmissions, which threatens communication efficiency and reliability. To this end, we first introduce a 3D simulation design that accurately models UL and DL transmissions within stochastic integrated terrestrial and non-terrestrial networks (SI-TNTNs), incorporating adaptive jamming strategies and employing a stochastic node distribution based on the Homogeneous Poisson Point Process (HPPP). Next, in order to train encoder networks deployed on clients for both UL and DL transmissions, we propose a distributed learning framework called the federated partial model aggregation (FedPMA) algorithm that uses the spectral correlation function (SCF) for feature representation. The proposed approach combines encoder parameter aggregation with a multivariate normal (MVN)-based probabilistic reliability estimation derived locally from encoder outputs, which guides the aggregation process. Only encoder parameters and scalar reliability scores are exchanged between clients and the parameter server (PS), while raw data and latent representations remain local. Our experimental results reveal that our approach not only enhances data privacy, but also significantly improves model performance and convergence, thus paving the way for the development of robust, interference-sensitive communications strategies suitable for next-generation non-terrestrial network (NTN)-enabled wireless systems.