The integration of Reinforcement Learning (RL) into robotic-assisted surgery (RAS) holds significant promise for advancing surgical precision, adaptability, and autonomous decision-making. However, the development of robust RL models in clinical settings is hindered by key challenges, including stringent patient data privacy regulations, limited access to diverse surgical datasets, and high procedural variability. To address these limitations, this paper presents a Federated Deep Reinforcement Learning (FDRL) framework that enables decentralized training of RL models across multiple healthcare institutions without exposing sensitive patient information. A central innovation of the proposed framework is its dynamic policy adaptation mechanism, which allows surgical robots to select and tailor patient-specific policies in real-time, thereby ensuring personalized and Optimised interventions. To uphold rigorous privacy standards while facilitating collaborative learning, the FDRL framework incorporates secure aggregation, differential privacy, and homomorphic encryption techniques. Experimental results demonstrate a 60\% reduction in privacy leakage compared to conventional methods, with surgical precision maintained within a 1.5\% margin of a centralized baseline. This work establishes a foundational approach for adaptive, secure, and patient-centric AI-driven surgical robotics, offering a pathway toward clinical translation and scalable deployment across diverse healthcare environments.

翻译：将强化学习（RL）整合到机器人辅助手术（RAS）中，对于提升手术精度、适应性和自主决策能力具有重要前景。然而，在临床环境中开发鲁棒的RL模型面临关键挑战，包括严格的患者数据隐私法规、多样化手术数据集获取受限以及手术过程的高变异性。为应对这些限制，本文提出一种联邦深度强化学习（FDRL）框架，支持在多个医疗机构间进行RL模型的去中心化训练，而无需暴露敏感患者信息。该框架的核心创新在于其动态策略适应机制，使手术机器人能够实时选择并定制针对特定患者的策略，从而确保个性化和优化的干预。为在促进协作学习的同时维护严格的隐私标准，FDRL框架整合了安全聚合、差分隐私和同态加密技术。实验结果表明，与传统方法相比，隐私泄露减少了60%，手术精度维持在集中式基线1.5%的误差范围内。本研究为自适应、安全且以患者为中心的AI驱动手术机器人技术奠定了方法论基础，为临床转化及在多样化医疗环境中的可扩展部署提供了可行路径。