For artificial intelligence to be safely deployed in high-risk domains, it must reliably know its limits. Selective prediction, or learning with a reject option, addresses this by enabling a model to abstain from prediction on inputs it deems unreliable, deferring them to a human expert. While deep ensembles have emerged as a leading approach for uncertainty estimation, their potential is often squandered by rejection methods that rely on static thresholds applied to the mean prediction. In this paper, we propose to learn a dynamic rejection policy directly from the rich behavioral signals of the ensemble itself. Our framework, DEGRE (Dynamic Ensembles Gating for REjection), is a novel meta-learning approach that trains a lightweight gating network on the ensemble’s consensus confidence and its internal disagreement (variance)— to explicitly discriminate between correct and incorrect predictions. Through rigorous evaluation across twelve diverse medical imaging benchmarks (MRI, X-ray, CT), DEGRE significantly advances selective prediction, achieving an average risk-coverage (AURC) reduction of 68.2% compared to the standard ensemble baseline. By providing a more reliable method for a model to recognize its own limitations, this learned, adaptive rejection mechanism paves the way for safer and more responsible integration of AI into critical clinical workflows.

Nowadays, billions of phones, internet-of-things (IoT), and edge devices around the world generate data continuously, enabling many machine-learning (ML)-based products and applications. However, due to increasing privacy concerns and regulations, these data tend to reside on devices (clients) instead of being centralized for performing traditional ML model training. Federated learning (FL) is a distributed approach in which a single server and multiple clients collaboratively build an ML model without moving data away from clients. Whereas existing studies on FL have their own experimental evaluations, most experiments were conducted using a simulation setting or a small-scale testbed. This might limit the understanding of FL implementation in realistic environments. In this empirical study, we systematically conduct extensive experiments on a large network of IoT and edge devices (called IoT-Edge devices) to present FL real-world characteristics, including learning performance and operation (computation and communication) costs. Moreover, we mainly concentrate on heterogeneous scenarios, which is the most challenging issue of FL. By investigating the feasibility of on-device implementation, our study provides valuable insights for researchers and practitioners, promoting the practicality of FL and assisting in improving the current design of real FL systems.

Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Gener-alization (FedDG) attempts to train a global model using collaborative clients in a privacy-preserving manner that can generalize well to unseen clients possibly with domain shift. However, most existing FedDG methods either cause additional privacy risks of data leakage or induce signifi-cant costs in client communication and computation, which are major concerns in the Federated Learning paradigm. To circumvent these challenges, here we introduce a novel architectural method for FedDG, namely gPerXAN11https://github.com/lhkhiem28/gPerXAN, which relies on a normalization scheme working with a guiding regularizer: In particular, we carefully design Personalized eXplicitly Assembled Normalization to enforce client mod-els selectively filtering domain-specific features that are bi-ased towards local data while retaining discrimination of those features. Then, we incorporate a simple yet effec-tive regularizer to guide these models in directly capturing domain-invariant representations that the global model's classifier can leverage. Extensive experimental results on two benchmark datasets, i.e., PACS and Office-Home, and a real-world medical dataset, Camelyon17, indicate that our proposed method outperforms other existing methods in ad-dressing this particular problem.

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Deep Learning (DL) has emerged as an efficient tool for the classification problem in electrocardiogram (ECG)-based SA diagnoses. Despite these advancements, most common conventional feature extractions derived from ECG signals in DL, such as R-peaks and RR intervals, may fail to capture crucial information encompassed within the complete ECG segments. In this study, we propose an innovative approach to address this diagnostic gap by delving deeper into the comprehensive segments of the ECG signal. The proposed methodology draws inspiration from Matrix Profile algorithms, which generate an Euclidean distance profile from fixed-length signal subsequences. From this, we derived the Min Distance Profile (MinDP), Max Distance Profile (MaxDP), and Mean Distance Profile (MeanDP) based on the minimum, maximum, and mean of the profile distances, respectively. To validate the effectiveness of our approach, we use the modified LeNet-5 architecture as the primary CNN model, along with two existing lightweight models, BAFNet and SE-MSCNN. Our experiment results on the PhysioNet Apnea-ECG dataset (70 overnight recordings), and the UCDDB dataset (25 overnight recordings) revealed that our new feature extraction method achieved per-segment accuracies of up to 92.11% and 81.25%, respectively. Moreover, using the PhysioNet data, we achieved a per-recording accuracy of 100% and yielded the highest correlation of 0.989 compared to state-of-the-art methods. By introducing a new feature extraction method based on distance relationships, we enhanced the performance of certain lightweight models in DL, showing potential for home sleep apnea test (HSAT) and SA detection in IoT devices. The source code for this work is made publicly available in GitHub: https://github.com/vinuni-vishc/MPCNN-Sleep-Apnea.