Federated Deep Reinforcement Learning for Privacy-Preserving Robotic-Assisted Surgery

2025-05-17

AI Summary

To address privacy sensitivity, data silos, and inter-patient heterogeneity in reinforcement learning (RL) for surgical robotics, this paper proposes a decentralized federated deep RL framework. The framework integrates secure aggregation, differential privacy, and homomorphic encryption to ensure end-to-end privacy compliance during cross-institutional collaborative training. It introduces a novel dynamic policy adaptation mechanism enabling real-time selection and fine-tuning of patient-specific surgical policies on robotic platforms. Additionally, online policy distillation is incorporated to enhance on-device deployment efficiency. Experiments demonstrate a 60% reduction in privacy leakage while maintaining surgical accuracy within 1.5% error of the centralized baseline—significantly outperforming existing federated RL approaches. This work represents the first solution enabling real-time, privacy-preserving, multi-center adaptation of personalized surgical policies, achieving both clinical applicability and large-scale scalability.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Intelligent Robots: Learning & Optimization for ROB
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

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.

Problem

• Ensuring patient data privacy in robotic surgery AI training
• Overcoming limited access to diverse surgical datasets
• Reducing procedural variability in robotic-assisted surgery

Innovation

• Federated Deep Reinforcement Learning for decentralized training
• Dynamic policy adaptation for real-time patient-specific interventions
• Secure aggregation, differential privacy, and homomorphic encryption