Federated Learning (FL) enables collaborative machine learning across decentralized devices and data sources, but resource constraints on pervasive devices necessitate efficient model compression. Existing approaches, such as quantization for on-device training, often degrade accuracy, especially for classes that are difficult to learn due to imbalance, poor-quality samples, or inherent complexity. This results in persistent accuracy gaps across classes. We propose Fed-CASQ's a novel framework that couples class-aware strategies into the quantization process to jointly improve efficiency and accuracy in pervasive FL. Unlike prior works that address quantization and imbalance separately, Fed-CASQ adaptively selects quantization levels based on device resources and leverages Layer-wise Relevance Propagation (LRP) to assess class-relevant convolutional neural network (CNN) filters on the client side. An adaptive weight scaling mechanism is then applied to amplify critical information for low-accuracy classes before aggregation. At the server, a complementary novel aggregation strategy mitigates global imbalance across clients, ensuring that underperforming classes receive proportional attention during model updates. We theoretically establish that Fed-CASQ achieves a convergence rate of ${\mathcal{O}}\left({\frac{{\kappa *\hat \sigma *\hat \delta }}{{\sqrt T }}}\right)$ under non-convex settings. We empirically establish that quantization directly influences the performance of under sampled (minority) classes. Experimental results further show that Fed-CASQ substantially narrows the performance gap for low-accuracy classes, improving their accuracy by ≈30%, while reducing training latency by over 56% on resource-constrained pervasive devices.

Monitoring respiratory rate (RR) is essential for early identification of respiratory and metabolic abnormalities. However, the limitations of contact-based sensors and the lack of reliability in many contactless methods make continuous and accurate monitoring difficult in non-clinical settings. To address these challenges, we introduce RespFormer, an edge-optimized, motion-guided temporal-frequency multimodal fusion transformer framework for real-time, contactless RR estimation and breathing pattern classification. RespFormer integrates dense optical flow analysis with temporal, statistical, and frequency-domain features derived from video sequences and enhances them through a multi-stage signal processing pipeline. These features are modeled using an ensemble of three time series transformer architectures (ETSformer, Temporal Fusion Transformer, and Informer) to capture distinct aspects of temporal dynamics. A shared attention-based refinement module enhances the feature representations, and final predictions are fused using a stacking-based meta-learner. We validate RespFormer on a multimodal dataset comprising synchronized RGB, NIR, and IR video data, including a custom in-house dataset captured under various conditions. Experimental results demonstrate that RespFormer achieves a mean absolute error (MAE) ≈ 0.98 bpm, improving prediction accuracy by ≈ 11% and reducing memory usage by ≈ 26%, while maintaining real-time inference (≈ 1.22 seconds) on resource-constrained devices. Furthermore, RespFormer accurately classifies breathing patterns (normal, bradypnea, tachypnea, and apnea) with 95% accuracy, underscoring it’s potential for practical application in telemedicine, clinical screening, and low-resource healthcare settings.

Continuous, non-invasive respiratory rate (RR) monitoring is essential for the early diagnosis of many medi-cal problems. However, conventional contact-based sensors frequently under-perform in dynamic situations that can be un-comfortable and require human intervention. To overcome these limitations, we propose E2RespUNet, an end-to-end system that uses multimodal video data to estimate breathing rates and reconstruct respiratory signals using an attention-enhanced U-Net architecture. Our method combines optical flow analysis with preprocessing, detrending, and normalization to reliably extract chest motion features in a variety of settings. We validated our approach using a collection of RGB, near-infrared (NIR), and infrared (IR) videos from multiple sources, such as an in-house RGB dataset and a publicly available sleep dataset. According to our study in both the temporal and frequency domains, E2RespUNet surpasses existing baseline models by lowering the mean absolute error by up to 21 % in the sleep dataset and by up to 28% in the in-house dataset. Additionally, E2RespUNet addresses real-time deployment requirements on resource-constrained edge devices by processing each frame in less than 1 ms (0.99 ms in the Sleep dataset and 0.97 ms in the in-house dataset). These findings, together with a low KL divergence and a power spectral density (PSD) that corresponds to the ground truth, suggest that E2Resp UN et is accurate and fast enough for real-time use in emergency and clinical scenarios.

The usage of Lithium-ion (Li-ion) batteries has gained widespread popularity across various industries, from powering portable electronic devices to propelling electric vehicles and supporting energy storage systems. A central challenge in Li-ion battery reliability lies in accurately predicting their Remaining Useful Life (RUL), which is a critical measure for proactive maintenance and predictive analytics. This study presents a novel approach that harnesses the power of multiple denoising modules, each trained to address specific types of noise commonly encountered in battery data. Specifically, a denoising auto-encoder and a wavelet denoiser are used to generate encoded/decomposed representations, which are subsequently processed through dedicated self-attention transformer encoders. After extensive experimentation on NASA and CALCE data, a broad spectrum of health indicator values are estimated under a set of diverse noise patterns. The reported error metrics on these data are on par with or better than the state-of-the-art reported in recent literature.