## 3 Related Work
### Federated Learning in Healthcare
By training models across multiple healthcarefacilities, researchers have achieved similar diagnostic performance to centralized methods while addressing data privacy, showing FL's adaptability in clinical imaging and diagnostics [49]. In addition, FL integrated with blockchain has demonstrated promising results in distributed healthcare environments by maintaining data integrity and securing model updates on a decentralized ledger. Blockchain-backed FL frameworks in healthcare offer solutions to trust and transparency challenges, thus fostering collaboration among institutions while ensuring robust model training [50]. In the literature, there are a lot of methods for secure aggregation, like Secure Multiparty Computation (SMPC), Differential Privacy (DP), and Homomorphic Encryption (HE). Despite these security measures, ongoing advancements are necessary to establish a secure aggregation FL-based system [51]. For example, while homomorphic encryption allows computations on encrypted data without exposing raw inputs, it often incurs significant computational overhead and degrades model accuracy, especially in deep learning settings [52, 53]. Similarly, SMPC and DP may reduce model utility or introduce noise that hampers prediction performance [54]. However, while cryptographic techniques safeguard data confidentiality, they do not always ensure anonymity or prevent linkability, critical in healthcare scenarios where re-identification could occur from aggregate patterns. Therefore, our Federated Learning setup utilizes differential privacy during the federated aggregation process to enhance privacy and protect the system against model inversion attacks and AES encryption to ensure that there is a security copy of the sensitive data collected during the gamified experience. Although DP may impact model performance due to injected noise, we tried to maintain a balance between data protection and predictive accuracy, since in our case, preventing re-identification attacks is paramount. Moreover, recent research has explored privacy-preserving Federated Learning in Internet of Things (IoT) environments. For example, FedSuper [55], a Federated Learning framework designed for IoT networks, incorporates adaptive trust management and secure, decentralized model training. FedSuper highlights how Federated Learning can be hardened against adversarial threats and made robust in dynamic, heterogeneous environments. FedSuper enhances Byzantine resilience by giving the server a trusted validation mechanism (shadow dataset), enabling it to detect and discard malicious updates before aggregation. While the framework focuses on low-level sensor applications, its core principles, such as decentralized trust and privacy-preserving aggregation, are highly relevant to mobile health ecosystems. However, FedSuper does not address critical aspects of healthcare deployments, such as user-level identity, informed consent, or legal compliance. In general, approaches that develop FL focus on data processes rather than securing the access points. In our research, we address this gap by implementing robust access control measures that adhere to various user policies. Different stakeholders, such as children, parents, or medical workers, need to access critical personal data. This personal information is highly sensitive, as it contains personal health details that necessitate secure access and minimal communication delays [56]. Strict regulations like GDPR have been established to govern the interpretation and utilization of this data. These regulations present numerous challenges for advanced data processing techniques, such as Machine Learning (ML) and Deep Learning (DL), which require a substantial amount of training data. Therefore, utilizing conventional ML techniques in the development of smart healthcare systems requires the sharing and communication of data with centralized servers or the cloud, which can lead systems to security breaches.