## Collaborative Research: Detecting and Preventing Covid-19 with Privacy-Preserving Decentralized Machine Learning

Bao Wang

We are facing scientific challenges caused by the COVID-19, including detecting COVID-19 accurately and preventing its spread efficiently. Cutting-edge machine learning technologies, especially modern deep learning arts, provide feasible avenues to resolve these challenges. Deep learning-based computational imaging algorithms facilitate accurate and rapid COVID-19 diagnosis; sequential modeling with recurrent neural networks or transformers enables accurate and real-time COVID-19 spread prediction. However, most existing black-box deep learning research on the COVID-19 is the alchemy of turning unstructured data into gold and based on systematic trial and error. The current deep learning-based COVID-19 research raises many untrustworthy issues, including unreliable diagnosis, data privacy sacrifice, and lack of interpretability. Lacking interpretable and reliable predictions puts substantial strains on practitioners to leverage deep learning approaches to detect and prevent COVID-19. Data privacy constraints bring us many unraveling challenges. Thus, developing trustworthy machine learning algorithms while preserving data privacy is crucial to detect and prevent COVID-19.

We are a team of researchers with different expertise and common research interests, who jointly seek to develop theoretically principled decentralized machine learning algorithms that can provide reliable predictions. Furthermore, we focus on applying these machine learning algorithms to accurately and rapidly diagnose COVID-19 patients and predict the virus spread. We propose a challenging but walkable path towards developing privacy-preserving machine learning algorithms to detect and prevent COVID-19. We will integrate our expertise synergistically to develop privacy-preserving decentralized machine learning algorithms with performance guarantees and a high-throughput and low-latency software package to accurately and rapidly detect COVID-19 and effectively prevent its spread. As such, we propose three interconnected thrusts to develop novel neural network architectures based on mathematical principles, efficient privacy-preserving decentralized optimization algorithms, algorithms for spatiotemporal data forecasting and medical image processing and analysis, and an integrated software package to assist fighting against the coronavirus. Each thrust contains multiple theoretical explorations and numerical validation.

**Intellectual Merit:**The proposal's intellectual merit include: (i) development of robust and mathematically principled recurrent neural networks for accurate real-time spatio-temporal forecasting, (ii) development of novel efficient federated and decentralized machine learning algorithms with a performance guarantee, (iii) leveraging the stochastic differential equations theory to develop new privacy-preserving machine learning mechanisms, (iv) construction of new epidemiology models-principled recurrent neural networks with accurate and interpretable predictions, (v) development of trustworthy deep learning-based frameworks for COVID-19 diagnosis from multi-modal medical measurements.

**Broader Impacts:**The broader impacts of this project are in applying the proposed algorithms and their analysis over a wide range of science and engineering disciplines, such as scientific and medical image analysis, epidemic forecasting, patient monitoring, and microscopic imaging. The projects shall train a diverse body of the graduate and undergraduate students at Michigan State University, the University of Kentucky, and the University of Utah through collaborative education and research activities in applied mathematics, statistics, computer science, data science, physics, and social science. The project also plans to have research activities involving under-represented students in three universities located in three states. Besides the interdisciplinary collaboration across other institutions, we also aim to establish industrial partnerships to extend the proposed project's impact. The developed software will be shared with the general public through Github.