## 3 Related Work
### Federated Learning in Healthcare  
Federated Learning (FL) is widely utilized in the medical field, where large volumes of data are collected with the aim of training models and producing meaningful outcomes, like in our work. FL [44] is a machine learning paradigm that enables collaborative model training across decentralized data sources, particularly useful in fields like healthcare, where data privacy and security are paramount. In healthcare, FL empowers institutions to use large-scale data across multiple hospitals or devices, enhancing predictive accuracy and enabling personalized treatment without requiring data centralization. This approach has led to advancements in diagnostic accuracy, patient monitoring, and privacy preservation, making FL a compelling solution in sensitive domains. To address the privacy challenges associated with machine learning model training, we adopted FL. FL enables privacy preservation by transferring only learnable model parameters rather than raw data, thus eliminating the need to share sensitive patient information. In our case, model training is performed locally on clinic tablets, and only the parameters are securely transferred, ensuring data confidentiality throughout the process. Furthermore, there is no research work that has specifically investigated the use of FL techniques in the mental health domain, in which ADHD is dedicated. Therefore, the authors in [7] aimed to bridge this gap by providing an in-depth background on using FL and its state-of-the-art techniques applied in the mental health field. Consequently, in our application, we leveraged FL to ensure data privacy in the highly sensitive environment of children's cognitive health monitoring. Recent studies highlight FL's potential and innovations in healthcare. One prominent application is anomaly detection for early-stage diagnosis of Autism Spectrum Disorder (ASD). This study employs a semi-supervised FL approach using AutoEncoder-based machine learning models and data from a specially designed serious game. This setup allows for early ASD detection while preserving user privacy through fully homomorphic encryption during model aggregation, reducing the risk of inference attacks. The results suggest that FL can accurately predict ASD risk while addressing the privacy needs of sensitive data, demonstrating the flexibility of FL for neurodevelopmental disorder screening [45]. In another study, FL is applied to breast cancer diagnosis by integrating Explainable AI (XAI) to enhance trust and interpretability. By training models on distributed datasets and computing SHAP values locally, researchers preserved privacy without sacrificing diagnostic accuracy. The model achieved high performance metrics, including a 97.59% accuracy on the Wisconsin Diagnostic Breast Cancer Dataset, using artificial neural networks. This study illustrates FL's ability to maintain privacy, enable interpretability, and support critical medical decisions in cancer diagnostics [46]. FL has also proven effective in IoT-based healthcare systems, where homomorphic encryption enhances security by protecting local models against inference attacks, allowing robust medical image analysis and diagnostic tasks. This has been particularly useful in scenarios where dataset quality varies across nodes, enabling more equitable contributions to global models and reducing computational overhead [47]. Furthermore, FL frameworks are increasingly adopted in remote health-monitoring systems, providing solutions for privacy-preserving patient tracking and chronic disease management. Studies show that using FL to train models with isolated datasets improves diagnostic accuracy, making it a suitable solution for distributed healthcare applications [48]. Another application emphasizes FL's ability to tackle data heterogeneity in diagnostic imaging, particularly in mammography.