CAREER: Trustworthy and Robust Federated Learning for Computational Healthcare

University Of Maryland Baltimore County, Baltimore MD

Investigators

Abstract

The National Academy of Engineering has identified ‘Advanced Health Informatics’ as one of the grand challenges of the 21st century for improving patient care and swiftly responding to public health emergencies. Computational healthcare – a data-driven machine learning approach for healthcare, has a tremendous potential to advance and revolutionize healthcare by supporting the evidence-based practice of medicine, personalizing patient treatments, and reducing costs. Unlocking this potential requires the seamless integration of health data from multiple stakeholders such as patients, hospitals, providers, and local, state, and national agencies, and protection against the dangers of compromise or misuse of the information. However, integrating health data from multiple stakeholders is prohibitive due to cost, patient privacy risk, and data protection regulations. To tackle these important challenges, stakeholders can collaborate to jointly build and evaluate machine learning models without sharing data using a decentralized machine learning approach called Federated Learning. However, using federated learning in the healthcare domain is fragile due to a multitude of challenges, including heterogeneous patient data and varying computational resources at the stakeholders’ sites, limited data availability for diseases and patients, and vulnerability to adversarial attacks on the data and models – all of these limit federated machine learning model development. This project addresses these fundamental challenges of federated learning for healthcare by pioneering next-generation robust and trustworthy federated learning algorithms and methods for the generation, assimilation, and analysis of heterogeneous data for computational healthcare applications, contributing to the national effort toward precision medicine initiatives. The project will have a real-world impact by accelerating medical discovery and aiding in clinical decision-making in several ways: (a) securely integrating and learning from distributed heterogeneous siloes of health data; (b) yielding robust representations of diseases and patients; (c) building clinicians’ and patients’ trust in the data-driven methods by providing a flexible open-sourced evaluation toolkit. This project will also have a significant educational and outreach impact via interdisciplinary research training and skills development of undergraduate and graduate students through coursework and real-world healthcare projects with medical experts. Directed efforts will be undertaken to broaden the participation of students and K-12 groups in STEM education. This project will develop new algorithms, methodologies, and software to improve data-driven federated learning for computational healthcare - by advancing the state-of-the-art in multi-view learning, adversarial learning, and machine unlearning. This project’s overarching theme is robustness and trustworthiness, and is organized into three interrelated thrusts. The first thrust of the project focuses on ‘robust federated learning’, and it involves developing multi-view federated learning algorithms and coded federated learning methods to address the statistical and system heterogeneity challenges inherent in healthcare settings. The second thrust of the project is focused on ‘trustworthy federated learning’, under which novel federated adversarial training and verifiable federated unlearning algorithms will be developed to achieve resiliency to adversarial attacks and data-deletion requests, and fair and interpretable algorithms will be employed to make the proposed federated learning solutions unbiased and equitable to all patients and stakeholders. Finally, in the third thrust of the project, the researchers will study and develop algorithms for generating and evaluating realistic synthetic health data for federated learning to enable reproducibility and accelerate the development of federated learning methods in healthcare. 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.