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

Hafeez, S ORCID: 0000-0003-4769-4284, Mulkana, SR, Imran, MA and Sevegnani, M (2025) Federated Deep Reinforcement Learning for Privacy-Preserving Robotic-Assisted Surgery. In: Proceedings 2025 IEEE 45th International Conference on Distributed Computing Systems Workshops Icdcsw 2025 , 00. pp. 689-699. (2025 IEEE 45th International Conference on Distributed Computing Systems Workshops (ICDCSW), 21st Jul-23rd Jul 2025, Glasgow).

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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 decentralised 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 personalised 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 centralised 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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	46 Information and Computing Sciences; 4602 Artificial Intelligence; 4604 Cybersecurity and Privacy; Bioengineering; Health Disparities; Machine Learning and Artificial Intelligence; Clinical Research; Health Disparities and Racial or Ethnic Minority Health Research; Patient Safety; Networking and Information Technology R&D (NITRD); Generic health relevance; 3 Good Health and Well Being
Subjects:	Q Science > QA Mathematics > QA76 Computer software R Medicine > RD Surgery
Divisions:	Computer Science and Mathematics
Publisher:	IEEE
Date of acceptance:	4 March 2025
Date of first compliant Open Access:	8 July 2026
Date Deposited:	08 Jul 2026 10:38
Last Modified:	08 Jul 2026 10:38
DOI or ID number:	10.1109/ICDCSW63273.2025.00128
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28953

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