A Privacy Preserving Federated Learning Framework for COVID-19 Vulnerability Map Construction

This paper presents a federated learning (FL) framework that uses multiple self-reporting crowdsourcing mobile and web apps to collaboratively construct a fine-grained COVID-19 vulnerability prediction map. The use of FL provides a reliable prediction by aggregating training results from multiple apps, while at the same time circumventing data privacy regulations that prevent user information from multiple apps to be shared with each other. Such a fine-grained vulnerability map identifies early on high-risk areas, helping to reduce the spread of the disease. To mitigate data bias from each self-reporting app, an adaptive worker selection algorithm that leverages neighbouring datasets to obtain a balanced data distribution is proposed. Further, a differential privacy scheme is adopted to protect user information. The simulation results show that the proposed framework outperforms the widely used FedAvg FL algorithm by 6% on prediction accuracy while preserving user privacy.