## 5 Discussion
First, although the model was evaluated on three public datasets, the overall sample size is still limited compared to real-world clinical populations. Second, the model mainly focuses on EEG, ECG, and basic respiratory signals; although signals such as SpO\({}_{2}\) and EMG are combined, they do not have a large weight tendency for the model due to data quality issues. In the future, improving data quality or based on other modalities such as audio and snoring may further improve classification performance. Third, the class imbalance in certain severity levels (e.g., mild vs. moderate SDB) may introduce performance bias, requiring further exploration of adaptive loss functions or data augmentation strategies. Addressing this issue may require more adaptive strategies, such as dynamic loss weighting [17] or advanced loss functions like focal loss [52], which can better handle imbalanced distributions during training. In addition, while FedAvg was effective in our setup, the investigation of more advanced federated optimization strategies such as FedProx [53], FedDyn [54], or personalization-aware schemes is left for future work. Moreover, while our current framework focuses on two tasks (sleep staging and SDB severity classification), its modular design can be extended to support additional tasks, such as insomnia detection, multimodal anesthesia monitoring, or longitudinal sleep health tracking. Such extensions would allow for more holistic and fine-grained characterization of sleep states in diverse clinical scenarios. Finally, we believe this framework provides a strong foundation, but future research should explore integration with state-of-the-art models (e.g., transformer-based encoders, contrastive representation learning) to further improve accuracy and enable more comprehensive analysis of sleep disorders, ultimately enhancing the clinical utility and interpretability of the framework. We also envision the deployment of this framework on wearable or non-contact sensing devices, where its decentralized nature and multimodal adaptability would offer unique advantages for real-world, privacy-preserving sleep monitoring. Looking ahead, several avenues can further enhance the proposed FMTL framework. Addressing class imbalance in SDB severity classification remains a critical challenge, especially for borderline categories such as mild and moderate cases. Future work may explore adaptive solutions such as dynamic loss weighting or advanced loss functions like focal loss to mitigate label skewness and improve model robustness. Additionally, while FedAvg proved effective in our setup, alternative federated optimization strategies such as FedProx, FedDyn, and personalization-aware methods should be investigated to better accommodate client heterogeneity and communication constraints. Importantly, the privacy-preserving design of our FMTL framework inherently aligns with regulatory requirements such as HIPAA and GDPR, as it ensures that sensitive health data remains on local servers and only model updates are shared. To further enhance privacy guarantees, future iterations could incorporate differential privacy techniques during local training or apply secure aggregation protocols at the server side, offering stronger protection against inference attacks and reinforcing clinical trust. Beyond the current dual task setting, the modular structure of the framework facilitates extension to additional applications, including insomnia detection, longitudinal sleep health tracking, and multimodal anesthesia monitoring. These extensions could support more comprehensive and fine-grained assessments of sleep-related conditions. Finally, integrating advanced model architecture such as transformer-based encoders or contrastive representation learning may further improve accuracy and interpretability. We also envision future deployment of this framework on wearable or contactless devices, enabling decentralized, real-time, and privacy-preserving sleep monitoring in both clinical and home settings.