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

arXiv: https://arxiv.org/abs/2505.12153 · HTML: https://mlsanity.com/paper/2505.12153/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 17 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 7 of 7)

- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [From Decision to Action in Surgical Autonomy: Multi-Modal Large Language Models for Robot-Assisted Blood Suction](https://mlsanity.com/paper/2408.07806/) (2024)
- [Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery](https://mlsanity.com/paper/2408.03208/) (2024)
- [Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks](https://mlsanity.com/paper/2402.05973/) (2024)
- [A Blockchain-Enabled Framework of UAV Coordination for Post-Disaster Networks](https://mlsanity.com/paper/2402.15331/) (2024)
- [Blockchain-Enhanced UAV Networks for Post-Disaster Communication: A Decentralized Flocking Approach](https://mlsanity.com/paper/2403.04796/) (2024)
- [Blockchain-Empowered Immutable and Reliable Delivery Service (BIRDS) Using UAV Networks](https://mlsanity.com/paper/2403.12060/) (2024)

## Abstract (arXiv)

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.