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

Abstract

The growing integration of the Internet of Medical Things (IoMT) into healthcare has amplified the need for secure and privacy-preserving artificial intelligence. Federated Learning (FL) has emerged as a pivotal paradigm for decentralized medical data processing; however, it still faces challenges concerning data confidentiality, trust management, and scalability. This review presents an extended theoretical comparison of two prominent privacy-preserving frameworks—Federated Learning with Differential Privacy (FL-DP) and Federated Learning with Blockchain (FL-BC)—to assess their suitability for ensuring data security, transparency, and regulatory compliance in IoMT environments. The FL-DP framework safeguards patient data through noise injection during model updates, offering mathematically proven privacy guarantees. Conversely, the FL-BC framework reinforces trust and integrity via immutable ledgers and consensus mechanisms such as Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT). Reviewing literature published between 2021 and 2025, this study examines trade-offs in privacy, scalability, latency, and energy efficiency, while highlighting emerging hybrid architectures that integrate both approaches. The findings reveal that FL-DP provides stronger privacy control, whereas FL-BC ensures verifiable trust and traceability—together forming the foundation for next-generation secure and trustworthy federated learning systems in IoMT-driven healthcare.