Trust-aware adaptive differential privacy for asynchronous federated learning in non-terrestrial networks

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data locality and reducing the need to share raw data. However, deploying FL over non-terrestrial networks (NTNs), including satellite communication systems, introduces challenges such as propagation delay, intermittent connectivity, heterogeneous link reliability, and asynchronous update arrival. These conditions can increase update staleness and reduce the reliability and utility of client contributions, while privacy-preserving mechanisms may further affect model performance. This paper proposes TAADP-FedAsync, a Trust-Aware Adaptive Differential Privacy framework for asynchronous federated learning in NTN environments. The framework jointly coordinates (i) trust-aware client evaluation based on update quality and communication conditions, (ii) trust-conditioned adaptive differential privacy under Rényi Differential Privacy (RDP), and (iii) trust- and staleness-aware asynchronous aggregation for delayed updates. By coupling these mechanisms, the framework enables client reliability information to influence privacy configuration and asynchronous aggregation, thereby providing a unified approach to the interaction between trust, privacy, and communication staleness. The framework is evaluated in a simulated NTN environment incorporating satellite propagation delay, Doppler effects, packet loss, handover dynamics, heterogeneous client reliability, and asynchronous updates. The evaluation includes baseline comparisons, component-wise ablation, parameter sensitivity analysis, and experiments with varying client populations and network conditions. In the reported experiments, TAADP-FedAsync achieves 37.14% test accuracy, compared with 34.9% for FedAvg and 36.6% for FedAsync. The observed improvement is associated with the combined effects of trust-aware client evaluation, adaptive noise scaling, and staleness-aware aggregation under the simulated NTN conditions.