Federated Reinforcement Planning for Privacy-Preserving Collaborative Large Language Model Agents
Keywords:
federated learning, reinforcement learning, large language model agents, privacy preservation, multi-agent planning, system architecture, decentralized governance, secure aggregationAbstract
The proliferation of large language model agents operating across decentralized, multi-stakeholder environments introduces unprecedented challenges for system-level coordination, data governance, and operational security. Traditional centralized training and inference paradigms expose sensitive user data and organizational knowledge to single points of compromise, while purely local agent learning fails to capture the systemic benefits of shared experience. This paper proposes Federated Reinforcement Planning, a novel architectural framework that integrates federated learning principles with reinforcement learning-based planning mechanisms to enable privacy-preserving collaboration among large language model agents. The framework is built upon a federated topology where agents update local planning policies using encrypted gradient aggregation, ensuring that raw interaction trajectories and proprietary reward signals never leave their trust boundaries. A key innovation lies in the decoupling of high-level planning guidance from low-level action execution, which allows agents to share abstract strategic knowledge without exposing the underlying data distributions or task-specific reasoning chains. This paper provides a detailed examination of the structural trade-offs inherent in such a system, including the tension between communication efficiency and model convergence, the robustness of decentralized governance mechanisms against adversarial agents, and the fairness implications of heterogeneous local computational resources. The analysis further addresses infrastructure requirements for secure aggregation, the sustainability of bandwidth-intensive coordination protocols, and policy considerations for cross-jurisdictional deployment. Through comparative discussion with existing multi-agent reinforcement learning and federated model aggregation approaches, the framework positions itself as a scalable and legally compliant alternative for enterprise, healthcare, and public-sector applications where data sovereignty is non-negotiable. The paper concludes with forward-looking perspectives on open challenges, including incentive mechanism design, differential privacy integration, and the governance of emergent agent behaviors in federated ecosystems.
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