A multi-layer architecture (the mobile-edge-cloud continuum) of federated learning for mobile health sensing data

Current federated learning architectures in mobile healthcare are limited to a centralised model without considering the full continuum of mobile-edge-cloud. Additionally, to support different data privacy needs of patients as well as the limitations of mobile environments, there is a need for considering a multi-level federated learning architecture for the mobile-edge-cloud continuum.

The project aims to improve efficiency and privacy of federated learning for mobile health sensing data by proposing a multi-level (mobile-edge-cloud continuum) federated learning architecture and develop context-aware models and schemes for optimising distribution, storage and processing of tasks and data in each layer.