SPHERE-DNA: Privacy-Preserving Federated Learning for eHealth

The rapid growth of chronic diseases and medical conditions (e.g. obesity, depression, diabetes, respiratory and musculoskeletal diseases) in many OECD countries has become one of the most significant wellbeing problems, which also poses pressure to the sustainability of healthcare and economies. Thus, it is important to promote early diagnosis, intervention, and healthier lifestyles. One partial solution to the problem is extending long-term health monitoring from hospitals to natural living environments. It has been shown in laboratory settings and practical trials that sensor data, such as camera images, radio samples, acoustics signals, infrared etc., can be used for accurately modelling activity patterns that are related to different medical conditions. However, due to the rising concern related to private data leaks and, consequently, stricter personal data regulations, the growth of pervasive residential sensing for healthcare applications has been slow. To mitigate public concern and meet the regulatory requirements, our national multi-partner SPHERE-DNA project aims to combine pervasive sensing tech-nology with secured and privacy-preserving distributed privacy frameworks for healthcare applications. The project leverages local differential privacy federated learning (LDP-FL) to achieve resilience against active and passive attacks, as well as edge computing to avoid transmitting sensitive data over networks. Combinations of sensor data modalities and security architectures are explored by a machine learning architecture for finding the most viable technology combinations, relying on metrics that allow balancing between computational cost and accuracy for a desired level of privacy. We also consider realistic edge computing platforms and develop hardware acceleration and approximate computing techniques to facilitate the adoption of LDP-FL and privacy preserving signal processing to lightweight edge processors. A proof-of-concept (PoC) multimodal sensing system will be developed and a novel multimodal dataset will be collected during the project to verify the concept.