Hierarchical Planning and Secure Knowledge Fusion for AI Agents: Combining Reinforcement Learning, Vertical Federated Learning, and Backdoor-Resilient Representations
Keywords:
Hierarchical reinforcement learning, vertical federated learning, backdoor defense, AI agent security, privacy-preserving machine learning, prototype consistency, system architecture, socio-technical governanceAbstract
The increasing deployment of autonomous AI agents in safety-critical and privacy-sensitive domains necessitates robust frameworks for intelligent decision-making, secure data collaboration, and adversarial resilience. This paper presents a unified architectural perspective that integrates hierarchical planning, reinforcement learning, vertical federated learning, and backdoor-resilient representations into a coherent system-level design. Hierarchical planning structures enable agents to decompose complex tasks into manageable subtasks, improving sample efficiency and interpretability while reducing computational overhead. Vertical federated learning provides a privacy-preserving mechanism for knowledge fusion across parties with disjoint feature spaces, allowing agents to learn richer representations without exposing raw data. However, the distributed nature of such systems introduces vulnerabilities to backdoor attacks, where malicious participants can embed hidden triggers that alter agent behavior under specific conditions. To address this challenge, we incorporate backdoor-resilient representation learning techniques, particularly prototype-consistency constraints and adversarial training, which safeguard against poisoning while preserving task performance. We analyze the structural trade-offs among scalability, privacy, robustness, and fairness across different layers of the system architecture. Governance and policy implications are discussed, including regulatory alignment, auditability, and equitable access to collaborative learning infrastructures. Through cross-domain comparisons and illustrative case studies in healthcare, autonomous navigation, and financial services, we demonstrate how the proposed framework balances competing objectives. This work contributes a holistic systems perspective that informs both future research directions and practical deployment strategies for secure and intelligent multi-agent ecosystems.
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