Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems

arXiv:2405.05611v1 Announce Type: cross
Abstract: Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients' mobile devices/sensors. At the same time, transferring the data to a central location for learning is certainly not an option, due to privacy concerns and legal issues, and in …

algorithms applications arxiv center cs.cr cs.lg data data center distributed domain edge entities federated federated learning health healthcare health systems hospitals machine machine learning mobile patients privacy systems training training data