FEEL: A Federated Edge Learning System for Efficient and Privacy-Preserving Mobile Healthcare

With the prosperity of artificial intelligence, neural networks have been increasingly applied in healthcare for a variety of tasks for medical diagnosis and disease prevention. Mobile wearable devices, widely adopted by hospitals and health organizations, serve as emerging sources of medical data and participate in the training of neural network models for accurate model inference. Since the medical data are privacy-sensitive and non-shareable, federated learning has been proposed to train a model across decentralized data, which involves each mobile device running a training task with its own data in parallel. However, due to the ever-increasing size and complexity of modern neural network models, it becomes inefficient, and may even infeasible, to perform training tasks on wearable devices that are resource-constrained. In this paper, we propose a FEderated Edge Learning system, FEEL, for efficient privacy-preserving mobile healthcare. Specifically, we design an edge-based training task offloading strategy to improve the training efficiency. Further, we build our system on the basis of federated learning to make use of distributed user data to improve the inference performance. In addition, during model training, we provide a differential privacy scheme to strengthen the privacy protection. A prototype system has been implemented to evaluate the training efficiency, inference performance and noise sensitivity, respectively. And the results have demonstrated that our proposal could train models in an efficient and privacy-preserving way.