Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems
This work addresses privacy and resource constraints in mobile-health systems, offering a solution for distributed learning in healthcare applications, but it appears incremental as it builds on existing federated learning methods.
The paper tackles the challenge of training machine learning models on distributed, sensitive health data from mobile and wearable devices without centralizing data, proposing a privacy-preserving edge federated learning framework. It demonstrates the framework's effectiveness through implementation on AWS for seizure detection in epilepsy monitoring, though no specific performance numbers are provided.
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 certain cases, because of the communication and computation overheads. Federated Learning (FL) is the state-of-the-art collaborative ML approach for training an ML model across multiple parties holding local data samples, without sharing them. However, enabling learning from distributed data over such edge Internet of Things (IoT) systems (e.g., mobile-health and wearable technologies, involving sensitive personal/medical data) in a privacy-preserving fashion presents a major challenge mainly due to their stringent resource constraints, i.e., limited computing capacity, communication bandwidth, memory storage, and battery lifetime. In this paper, we propose a privacy-preserving edge FL framework for resource-constrained mobile-health and wearable technologies over the IoT infrastructure. We evaluate our proposed framework extensively and provide the implementation of our technique on Amazon's AWS cloud platform based on the seizure detection application in epilepsy monitoring using wearable technologies.