## 5 Discussion
Among them, EOG's capture of micro-eye movement signals can help identify slow rolling eye movements in N1; EMG can reflect subtle changes in muscle tension, which helps to distinguish the difference between low-tension N1 and fully awake states; and ECG and respiratory signals also show certain autonomic nervous system adjustment characteristics during the N1 period. This modal information jointly participates in temporal modeling in the shared encoder, enabling the model to recognize the \"dynamic transitional\" characteristics of N1 rather than relying solely on vague static patterns under a single modality. Table 6 provides a summary of several representative studies, including details on the authors, network architecture, input modalities, output tasks, datasets, and reported results. These works span a range of methodologies, from deep learning models such as CNNs and RNNs to classical machine learning approaches like random forests and gradient boosting. However, due to variations in dataset composition, input modalities, and experimental protocols, it remains difficult to make direct and fully fair performance comparisons across studies. For instance, differences in signal preprocessing, segment lengths, subject populations, and evaluation metrics (e.g., cross-validation vs. independent test sets) all contribute to discrepancies in reported outcomes. Nonetheless, despite these inherent challenges in cross-study comparability, our proposed FMTL framework achieves consistently competitive or superior results across multiple public datasets, demonstrating its robustness and adaptability. From a deployment standpoint, the proposed framework is designed to operate under practical computational constraints. During federated training, each client only performs lightweight local updates with five epochs and a batch size of 32, which can be executed efficiently on standard institutional servers equipped with mid-range GPUs or even modern CPUs. The model architecture, composed of compact CNN, GRU, and attention layers, was deliberately chosen for its balance between expressiveness and computational efficiency. For low-resource settings, such as small clinics or edge devices, the inference phase requires minimal resources and can be executed in near real time. Local fine-tuning or participation in training can be scheduled during idle periods to minimize system load. These design choices ensure that the framework is feasible for deployment across diverse healthcare environments, including those with limited computational capacity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors/Network** & **Method** & **Input** & **Output** & **Dataset** & **Results** \\ \hline U-Sleep [46] & CNN model & Majority vote & Sleep stages & SHHS & MF1 score 80.0\% \\ SeeSleepNet [47] & CNN model and & C4-A1, EOG, EMG & Sleep stages & SHHS & MF1 score 78.5\%, \(\times\) 0.81 \\ SSC-SleepNet [17] & CNN and ResNet & EEG & Sleep stages & SHHS & MF1 score 84.0\%, \(\times\) 0.86 \\ XSleepNet1 [48] & CNN and RNN model & C4-A1, EOG, EMG & Sleep stages & SHHS & MF1 score 80.7\%, \(\times\) 0.83 \\ [PERSON] et al. [49] & An extreme gradient & Demographic features & & Full-night audio signals & \\  & boosting classifier & and features from & AHI estimation & from 172 subjects, cross-validation & \\  & & & Features using & & \\  & & & Autoregressive modeling & & ECG from 70 subjects \\ [PERSON] [50] & Random forest & classifier & and spectral & & (Apnea-ECG database) & \\  & & & Autocorrelation from & cross-validation & \\  & & ECG & & & \\ [PERSON] et al. [51] & RNN model & IBI and EDR & Event based detection & 9869 recordings from different datasets, & Sensitivity = 0.709, specificity = 0.734, \\  & & & & 1051 for testing & F1 = 0.721 \\ \hline \hline \end{tabular} Note: For the majority vote in U-Sleep, the hyprograms were generated using predictions from all available EEG-EOG channel combinations within each record.  
\end{table} 
Table 6: Summary of literature for comparison. However, several limitations remain in the current study.