Recent advancements in machine learning have improved performance while also increasing computational demands. While federated and distributed setups address these issues, their structures remain vulnerable to malicious influences. In this paper, we address a specific threat: Byzantine attacks, wherein compromised clients inject adversarial updates to derail global convergence. We combine the concept of trust scores with trial function methodology to dynamically filter outliers. Our methods address the critical limitations of previous approaches, allowing operation even when Byzantine nodes are in the majority. Moreover, our algorithms adapt to widely used scaled methods such as Adam and RMSProp, as well as practical scenarios, including local training and partial participation. We validate the robustness of our methods by conducting extensive experiments on both public datasets and private ECG data collected from medical institutions. Furthermore, we provide a broad theoretical analysis of our algorithms and their extensions to the aforementioned practical setups. The convergence guaranties of our methods are comparable to those of classical algorithms developed without Byzantine interference.

Electrocardiogram (ECG) delineation plays a crucial role in assisting cardiologists with accurate diagnoses. Prior research studies have explored various methods, including the application of deep learning techniques, to achieve precise delineation. However, existing approaches face limitations primarily related to dataset size and robustness.In this paper, we introduce a dataset for ECG delineation and propose a novel self-trained method aimed at leveraging a vast amount of unlabeled ECG data. Our approach involves the pseudolabeling of unlabeled data using a neural network trained on our dataset. Subsequently, we train the model on the newly labeled samples to enhance the quality of delineation. We conduct experiments demonstrating that our dataset is a valuable resource for training robust models and that our proposed self-trained method improves the prediction quality of ECG delineation.

Stress has become a ubiquitous challenge, affecting individuals mental and physical health significantly. Early detection of stress can aid in timely interventions, mitigating long-term health risks. Several studies focus on inter-subject stress classification. However, these studies often face limitations due to the small size and non-diversity of datasets. This paper introduces a novel framework based on deep learning for inter-subject stress detection utilizing electrocardiography data. We propose a two-step methodology to increase stress classification accuracy. The study involves the application of self-supervised contrastive learning and a data pseudo-labeling approach. We utilized PTB-XL, a large ECG dataset, to create a diverse training dataset. The model shows 93% by the G-mean metric on the publicly available WESAD dataset. This approach does not require the presence of ECG samples from the participant in the training set in order to correctly identify stress manifestations in the ECG.