The increasing complexity and scale of cyber threats necessitate advanced and adaptive defense mechanisms. Traditional reinforcement learning (RL)-based network defense systems often face challenges related to centralized data collection, privacy concerns, and scalability. To address these issues, this paper proposes a conceptual framework for Federated Reinforcement Learning (FRL) in network defense systems. The framework enables decentralized learning by allowing multiple entities to collaboratively train a global model while preserving data privacy. Key components of the framework include secure data-sharing mechanisms, privacy-preserving communication protocols, and trust management strategies to ensure reliable collaboration among participating entities. By integrating federated learning (FL) with RL, the proposed approach enhances threat detection, anomaly response, and real-time adaptability in dynamic network environments. Additionally, this framework minimizes data exposure risks while maintaining model accuracy, making it suitable for large-scale, privacy-sensitive network defense applications. The paper further discusses the challenges and potential solutions associated with implementing FRL in cybersecurity, paving the way for a more robust and privacy-aware defense strategy.