## 10 Challenges, Limitations, and Future Work  
During the setup of each relying party, key exchanges take place. While using a general verifier app could be an effective solution, finding an online method to authenticate trusted key exchanges is challenging. To address this, we implemented a desktop application as a trusted approach. Ideally, this process could be streamlined by installing a web browser add-on. However, there is a risk that the verification process could be bypassed, allowing unauthorized entities to display their content. Therefore, integrating additional partners, such as hospitals or external healthcare providers, requires installation and technical setup, which may hinder scalability and adoption. In addition, since no specific blockchain network or smart contract platform is inherently adopted, the organization itself is responsible for developing and managing the infrastructure, such as distributing cryptographic material and deploying virtual Identity Providers. This could be particularly demanding in the absence of dedicated technical staff, and the complex initial setup combined with the high cost of institutional integration further amplify the challenge.  
Moreover, while user consent mechanisms exist, they need further refinement to ensure seamless and explicit user approval for all data-sharing scenarios. For example, when a clinician updates a medical report, the system should promptly notify the parent to ensure they remain informed.  
Additionally, the implementation of differential privacy was a critical aspect of ensuring the confidentiality of sensitive medical data. While it effectively protected user information by introducing calibrated noise to the model updates, this approach introduced a trade-off between privacy and accuracy. By carefully tuning the privacy parameters, we were able to maintain a balance that preserved data privacy while ensuring the model's utility for clinical applications.  
Another key challenge identified by our collaboration with clinicians was the data availability problem, particularly in scenarios where children lost access to their devices or data became inaccessible due to hardware failure. This situation can have a negative impact on a participant's overall monitoring procedure, thus making the system difficult to use. To address this, we implemented AES-256 encryption for secure backup and storage of critical data, ensuring it could be safely recovered and restored without compromising privacy.  
Despite these advancements, certain limitations remain open for future exploration. For instance, our current implementation relies on the FedAvg aggregator for Federated Learning, which has limitations in handling highly heterogeneous data. Incorporating more sophisticated aggregation methods, such as FedProx or adaptive aggregators, is a key area for future work to better address client variability. Additionally, while the system has demonstrated its effectiveness in pilot studies, further testing with larger datasets is needed to fully validate its scalability and robustness in diverse real-world environments. These open areas represent significant opportunities to refine and expand the system's capabilities in privacy-preserving healthcare applications.  
Enhanced personalization will make the platform more inclusive and adaptable to diverse user needs, while expanded diagnostic capabilities promise broader utility across a range of mental and developmental health conditions. Incorporating sensor data and fostering interoperability with existing digital health platforms will further enrich the platform's functionality, enabling comprehensive, multi-modal assessments and seamless integration into healthcare ecosystems.