(untitled) (Journal of Advanced Artificial Intelligence Research, 2026)
The integration of large language models into federated and vertical split learning systems introduces unprecedented challenges in maintaining service integrity under adversarial manipulation. This paper presents FedPlanGuard, a planning-aware framework for detecting and mitigating backdoor attacks in distributed learning environments where LLM-driven applications operate across heterogeneous client nodes. Unlike conventional defenses that operate at the parameter or gradient level, FedPlanGuard leverages high-level planning signals extracted from model reasoning trajectories to identify anomalous behavior patterns that indicate backdoor injection. The framework operates within a socio-technical infrastructure that spans governance protocols, client trust assessment, and adaptive mitigation scheduling. We examine the structural trade-offs between detection sensitivity, communication overhead, and model utility, and demonstrate how planning-aware mechanisms can distinguish between benign distribution shifts and malicious manipulation. Through a cross-domain analysis of vertical split learning architectures, we show that planning consistency metrics provide a robust signal for backdoor detection even when gradient perturbation is minimal. The paper also addresses policy implications for deployment in critical infrastructures, emphasizing fairness constraints and auditability requirements. FedPlanGuard contributes a system-level perspective that bridges planning intelligence, secure aggregation, and decentralized oversight, offering a scalable path toward trustworthy LLM-driven applications in federated ecosystems.