用联邦强化学习让手术机器人安全学操作，保护隐私还更精准。
Federated Deep Reinforcement Learning for Privacy-Preserving Robotic-Assisted Surgery
- 多医院协作训练手术机器人，数据不出院且用加密技术保隐私。
- 隐私泄露减少60%，手术精度仅比集中式训练低1.5%。
- 适合关注医疗AI安全落地的临床与工程团队。
将强化学习（RL）融入机器人辅助手术（RAS）有望提升手术精度、适应性和自主决策能力。然而，临床环境中鲁棒性RL模型的发展受限于严格的患者数据隐私法规、缺乏多样化的手术数据集以及高程式的可变性。为此，本文提出一种联邦深度强化学习（FDRL）框架，实现跨多家医疗机构的去中心化模型训练，同时不暴露敏感患者信息。该框架的核心创新在于动态策略自适应机制，使手术机器人可实时选择并定制患者特定策略，确保个性化和优化干预。为在保障严格隐私的同时促进协作学习，FDRL框架融合了安全聚合、差分隐私和同态加密技术。实验结果表明，相比传统方法，隐私泄露降低60%，手术精度保持在集中式基线的1.5%以内。本工作为自适应、安全且以患者为中心的AI驱动手术机器人建立了基础路径，推动其向临床转化及多样化医疗环境中的规模化部署。
原文摘要 · Abstract (English)
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.
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