## 2 Related Work  
The integration of blockchain technology with federated learning in IoMT networks has emerged as a significant research area in recent years, driven by concerns over data privacy and security vulnerabilities in healthcare systems. In this section, we will analyze some of the most recent advances in this area, focusing on three main aspects: blockchain mechanisms, federated learning in healthcare, and integrated decentralized blockchain-FL  
Figure 1: EdgeGuard: a secure federated learning framework for IoMT. architectures. In the context of IoMT networks, ref. [8] presented a security framework that provides security when medical data are being transferred based on a combination of encryption techniques, pattern recognition modules, and adaptive learning mechanisms. Although this approach has made advances in both the detection of anomalies as well as attack resistance, it neither considers the quality of data in a distributed setting nor does it take into account the computational capabilities of IoMT products. In addition, the framework lacks mechanisms for enabling decentralized yet secure collaborative machine learning across healthcare institutions--a must-have component to enhance diagnostic models while guaranteeing the privacy of records. This shortcoming is something EdgeGuard addresses with its blockchain-secured federated learning architecture. [PERSON] et al. [15] proposed a federated learning paradigm toward collaborative usage of health information while keeping it private based on both secure multi-party computations and differential privacy. Even though it has provided significant privacy preservation, there has been no proper consideration given to securing the model updates, the participating devices, or the data quality. A recent work proposed by [PERSON] et al. [16] introduces a blockchain-supported federated learning system for WBANs, focusing on energy efficiency and privacy through QNNs, differential privacy, and homomorphic encryption. However, their approach primarily focuses on energy optimization and basic privacy preservation, with no consideration of data quality assessment and device reliability in medical contexts. The authors failed to provide explicit security requirements for IoMTs in healthcare environments. EdgeGuard bridges this gap through its adaptive aggregation and lightweight consensus mechanisms, which are specifically tailored for IoMT. Prior work by [PERSON] et al. [17] proposed an I-UDEC framework combining blockchain, AI, and federated learning to optimize computation offloading and resource allocation in ultra-dense edge computing. The work obtained great improvements in task execution time but was mainly focused on general IoT scenarios and did not consider specific medical data sensitivity and healthcare regulatory compliance. Furthermore, their blockchain implementation was not designed for resource-constrained medical devices--something EdgeGuard addresses in its healthcare-specific design and lightweight consensus mechanism. The paper by [PERSON] et al. [18] explored the integration of federated learning in healthcare Metaverse applications, highlighting potential benefits and challenges in medical diagnosis, patient monitoring, and drug discovery. While comprehensive in scope, the paper primarily focused on theoretical aspects and future possibilities, lacking practical implementation details or specific solutions for current IoMT security and privacy challenges that EdgeGuard addresses through its concrete blockchain-secured federated learning architecture. Previous research [19] proposed combining DFT with differential privacy in federated learning for healthcare, achieving better accuracy and reduced communication costs, but their approach lacked security mechanisms for model updates and did not address device reliability or data quality validation in medical networks--gaps that EdgeGuard specifically addresses.