In clinical sleep medicine, the gold standard for diagnosing sleep disorders is overnight polysomnography (PSG) [6], which records a wide range of physiological signals such as electroencephalogram (EEG), electroocologram (EOG), and electromyogram (EMG), as well as airflow and/or pressure [7], thoracabdominal movements [8], electrocardiogram (ECG), and blood oxygen saturation (SpO\({}_{2}\)). The apnea-hypopnea index (AHI), defined as the total number of apnea and hypopnea events divided by total sleep time (TST), is commonly used for the preliminary assessment of SDB severity [9]. Traditionally, while PSG provides comprehensive and accurate diagnostic information, it is limited by long waiting lists, complex setup, high costs, and the discomfort caused by multiple sensors and wires during measurement [10; 11]. These limitations reduce the accessibility of PSG-based diagnosis and make it unsuitable for long-term SDB monitoring. To address these limitations, home sleep apnea testing (HSAT) devices with simplified sensors have gained increasing attention as alternative tools for SDB diagnosis and AHI estimation [12; 13]. However, HSAT still requires nontrivial setup, manual analysis, and interpretation, and often relies on total recording time rather than actual TST, which may lead to underestimation of AHI [13]. Therefore, there is a strong demand for more cost-effective, accessible, and scalable solutions for SDB monitoring and AHI assessment. In recent years, researchers have explored the use of various single-channel or simplified multichannel physiological signals--including respiratory [14], heart rate [15], ECG [16], EEG [17], and audio [9]--for sleep staging and SDB severity assessment. For example, [PERSON] [9] proposed a multi-task learning approach using cardiac and audio signals for SDB severity classification and AHI estimation, achieving an accuracy of 57.8% for SDB severity classification on the SOMNIA dataset. [PERSON] [18] proposed a multi-task learning model for sequence signal reconstruction and sleep staging, achieving 85.6% accuracy for sleep staging on the SHHS dataset, demonstrating the potential of multi-task learning networks. [PERSON] [19] proposed an RGMNet, which achieves sleep staging accuracies of 86.61%. Furthermore, different physiological modalities capture complementary aspects of SDB pathophysiology. For instance, ECG enables the extraction of surrogate respiratory patterns and RR intervals through heart rate variability (HRV) analysis, which can assist in SDB severity assessment, while EEG remains the primary signal for sleep staging. Therefore, integrating multiple physiological signals can enhance the performance of both SDB monitoring and sleep staging. Although these two tasks share common physiological foundations and intrinsic correlations [20], studies on unified multi-task learning frameworks capable of simultaneously addressing both tasks remain limited [21]. At the same time, increasing emphasis on data privacy and security has made it difficult to aggregate sleep data across hospitals, centers, or devices, which severely limits the generalizability of deep learning models in real-world clinical settings [22]. Moreover, real-world sleep data collected from different institutions often exhibit non-independent and identically distributed (non-IID) characteristics due to variations in population demographics, sensor configurations, and annotation standards. These distributional discrepancies pose significant challenges for collaborative training and can degrade model performance if not properly addressed [23; 24]. To overcome these obstacles, federated learning (FL) [25; 26] has emerged as a promising paradigm for collaborative model training across institutions, enabling improved generalization and privacy preservation without sharing raw data.