S-HSFL: A Game-Theoretic Enhanced Secure-Hybrid Split-Federated Learning Scheme for UAV-Assisted Wireless Networks

Hybrid Split Federated Learning (HSFL for short) in emerging 6G-enabled UAV networks faces persistent challenges in data protection, device trust management, and long-term participation incentives. To address these issues, this study introduces S-HSFL, a security-enhanced framework that embeds verifiable federated learning mechanisms into HSFL and incorporates digital-signature-based authentication throughout the device selection process. This design effectively prevents model tampering and forgery attacks, achieving a defense success rate above 99%. To further strengthen collaborative training, we develop a MAB-GT device selection strategy that integrates multi-armed bandit exploration with multi-stage game-theoretic decision models, spanning non-cooperative, coalition, and repeated games, to encourage high-quality UAV nodes to provide reliable data and sustained computation. Experiments on the Modified National Institute of Standards and Technology (MNIST) dataset under both Independent and Identically Distributed (IID) and non-IID conditions demonstrate that S-HSFL maintains approximately 97% accuracy even in the presence of 30% adversarial UAVs. The MAB-GT strategy significantly improves convergence behavior and final model performance, while incurring only a 10–30% increase in communication overhead. The proposed S-HSFL framework establishes a secure, trustworthy, and efficient foundation for distributed intelligence in next-generation 6G UAV networks.