Award Abstract #2031799

RAPID: SaTC: FACT: Federated Analytics based Contact Tracing for COVID-19

See grant description on NSF site

Program Manager:

James Joshi

Active Dates:

Awarded Amount:



Lalitha Sankar

Ming Zhao

Ni Trieu

Awardee Organization:

Arizona State University


Computer and Information Science and Engineering (CISE)


The spread of the Corona Virus Disease 2019 (COVID-19), a highly-infectious disease caused by a newly discovered coronavirus, has reached pandemic levels across the globe. As the numbers in the USA of infections, critical care interventions and deaths continue to rise, mobile applications (apps) that enable contact tracing (CT) are being rapidly deployed to monitor the spread of COVID-19. However, on-going deployments, based on anonymously sharing tokens without exploiting the rich local device data, are insufficient in monitoring disease spread in a timely manner and are also vulnerable to privacy and security attacks. There is an urgent need to develop CT apps that not only monitor but also intervene to limit COVID-19 spread while respecting user security and privacy. This project addresses this challenge via Federated Analytics based Contact Tracing (FACT), a refined federated learning approach to leverage both device-level data and server capabilities in a private and secure manner. FACT enables prevention and intervention by including hotspot identification, user alerts, and continual assessment of user COVID-19 risk. FACT guarantees a private and secure way to (i) evaluate a user's need for testing or their resilience to exposure, and (ii) assess herd immunity across the population.FACT addresses both the vulnerability of current Bluetooth-based systems to a variety of attacks and limited learning at the server by proposing a secure GPS+Bluetooth system which will enable the server to detect geographical infection clusters in a privacy-preserving manner. FACT also harnesses the rich device-level mobility and acoustic sensing data to periodically predict risks of those exposed to COVID-19 positive patients using federated learning and without sharing any device data with the server. Several simple, but well-validated, parameters are extracted from these sensors to develop local digital markers of COVID risk. At the heart of these innovations are the refinements FACT brings to standard federated learning via knowledge distillation and model compression. This project, with the support of ASU University Technology Office and collaboration with industry companies, will deploy and evaluate FACT via a mobile app and reach many users. FACT can extend the clinical utility of acoustic measures, adopted in general clinical trials, for use in COVID-19 patients. This research also provides immense opportunities to train and expose diverse graduate students to the technical challenges of ensuring privacy and security while simultaneously enabling socially beneficial technologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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