Federated learning enables privacy-preserving distributed intelligence but faces challenges in balancing computation, communication, and privacy in heterogeneous networks. To address these issues, this paper proposes a privacy-preserving U-shaped split federated learning (USFL) framework for space–air–ground–sea integrated networks. The proposed architecture combines split learning and federated learning in a U-shaped structure, ensuring that both raw data and labels remain localized at client devices. In addition, a differential privacy mechanism is introduced to perturb intermediate features during transmission, enhancing resistance to inference attacks. A mathematical framework is established to model the learning process under resource constraints, and the convergence behavior and privacy loss are theoretically analyzed. Experimental results on the SeaShips dataset demonstrate that the proposed method achieves competitive accuracy compared with centralized and existing distributed approaches, while reducing communication overhead and improving privacy protection. These results validate the effectiveness of the proposed framework for secure and efficient distributed learning in complex network environments.
Sun et al. (Sat,) studied this question.