RP-IoMT: A Robust and Provable Framework for Federated Learning Privacy-Preserving Intelligence in Healthcare IoMT

Federated learning (FL) has emerged as a promising approach for enabling collaborative model training across distributed Internet of Medical Things (IoMT) devices without sharing sensitive data. Existing FL frameworks face significant challenges in healthcare settings, including vulnerability to adversarial attacks, lack of verifiable update integrity, and limited robustness under heterogeneous data distributions. These limitations hinder reliable deployment in critical medical applications. To address these challenges, this paper proposes RP-IoMT, a robust and privacy-preserving FL framework that integrates secure multi-party computation (MPC), zero-knowledge proof-based gradient verification, and robust aggregation mechanisms. The objective of this work is to ensure both the correctness and integrity of model updates while maintaining strong privacy guarantees in adversarial IoMT environments. RP-IoMT enforces bounded client updates using a zero-knowledge clipping protocol (ZKClip), performs secure aggregation using threshold-based MPC, and incorporates robust filtering techniques to mitigate poisoning and backdoor attacks. Experimental results on healthcare datasets demonstrate that RP-IoMT achieves improved predictive performance, reduced attack success rates, and stable convergence under both independent and identically distributed (IID) and non-IID conditions. These results indicate that the proposed framework provides a practical and reliable solution for secure and robust FL in real-world medical Internet of Things (IoT) systems.