IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
39255189
Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this ch...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
39150815
Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial inf...
Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, an...
Neural networks : the official journal of the International Neural Network Society
39096754
Graph neural networks (GNN) are widely used in recommendation systems, but traditional centralized methods raise privacy concerns. To address this, we introduce a federated framework for privacy-preserving GNN-based recommendations. This framework al...
In this article, I propose an ethical analysis of assistive domestic robots for older users. In doing so, I illustrate my inquiry with the example of ROB-IN assistive robot. ROB-IN is a Spanish project which is devoted to developing a robot that will...
Studies in health technology and informatics
39234730
INTRODUCTION: Retrieving comprehensible rule-based knowledge from medical data by machine learning is a beneficial task, e.g., for automating the process of creating a decision support system. While this has recently been studied by means of exceptio...
Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy. In the healthcare domain, FL enables collaborative training among diverse medical devices and ...
Neural networks : the official journal of the International Neural Network Society
39488109
Sharpness-aware minimization (SAM) aims to enhance model generalization by minimizing the sharpness of the loss function landscape, leading to a robust model performance. To protect sensitive information and enhance privacy, prevailing approaches add...
Neural networks : the official journal of the International Neural Network Society
39383677
Federated Learning (FL) allows multiple data owners to build high-quality deep learning models collaboratively, by sharing only model updates and keeping data on their premises. Even though FL offers privacy-by-design, it is vulnerable to membership ...
PURPOSE: Collaboration provides valuable data for robust artificial intelligence (AI) model development. Federated learning (FL) is a privacy-enhancing technology that allows collaboration while respecting privacy via the development of models withou...