A Theoretical Comparison of Federated Learning with Differential Privacy and Blockchain for Security and Privacy in IoMT

Abstract

The advancement of decentralized, real-time data collection through the Internet of Medical Things is transforming the healthcare industry. However, this innovation brings forth significant privacy, security, and scalability challenges. Federated Learning offers a reliable solution by enabling distributed machine learning while preserving data localization. This paper introduces two frameworks-Federated Learning combined with Differential Privacy and Blockchain-enhanced Federated Learning-to enhance robustness in IoMT systems. We compare these frameworks theoretically, evaluating their effectiveness in mitigating risks related to data confidentiality, adversarial resilience, scalability, and computational efficiency. FL-DP provides formal privacy guarantees through differential privacy techniques but is limited by the need to manage the privacy budget (E), especially in large-scale deployments. Alternatively, Blockchain-based FL maintains data integrity and decentralized trust using consensus mechanisms such as Proof of Work and Proof of Stake, but it encounters challenges related to scalability and computational efficiency. Our findings suggest that the choice between FL-DP and Blockchain-based FL depends on the specific security and privacy requirements of the IoMT application. FL-DP is better suited for privacy-critical applications where strict data confidentiality is paramount, while Blockchain-based FL is more appropriate when data integrity and trust are the primary concerns.