CAREER: Trustworthy and Robust Federated Learning for Computational Healthcare

美国国家工程院已将“先进健康信息学”确定为21世纪改善患者护理和快速应对突发公共卫生事件的重大挑战之一。计算医疗保健-一种数据驱动的医疗保健机器学习方法，通过支持循证医学实践、个性化患者治疗和降低成本，具有推动和变革医疗保健的巨大潜力。释放这一潜力需要无缝集成来自患者、医院、提供者以及地方、州和国家机构等多个利益攸关方的健康数据，并保护其免受信息泄露或滥用的危险。然而，由于成本、患者隐私风险和数据保护法规的原因，集成来自多个利益相关者的健康数据是令人望而却步的。为了应对这些重要挑战，利益相关者可以协作，共同构建和评估机器学习模型，而无需使用称为联合学习的去中心化机器学习方法共享数据。然而，在医疗保健领域使用联合学习是脆弱的，因为存在大量挑战，包括不同的患者数据和利益相关者站点上的不同计算资源，疾病和患者的数据可用性有限，以及对数据和模型的敌意攻击的脆弱性-所有这些都限制了联合机器学习模型的开发。该项目通过开创下一代稳健可靠的联合学习算法和方法，为计算医疗保健应用程序生成、同化和分析异类数据，为国家精准医疗计划做出贡献，解决了医疗保健联合学习的这些根本挑战。该项目将通过几种方式加速医学发现和协助临床决策，从而产生现实世界的影响：(A)安全地整合和学习分散的、不同种类的健康数据；(B)产生关于疾病和患者的可靠表示；(C)通过提供灵活的开源评估工具包，建立临床医生和患者对数据驱动方法的信任。该项目还将通过跨学科研究培训和本科生和研究生的技能发展，通过课程作业和与医学专家合作的现实世界卫生保健项目，产生重大的教育和推广影响。将采取有针对性的努力，扩大妇女、代表性不足和K-12群体在STEM教育中的参与。该项目将开发新的算法、方法和软件，通过推进多视角学习、对抗性学习和机器遗忘方面的最先进技术，来改进用于计算医疗保健的数据驱动的联合学习。这个项目的总体主题是健壮性和可信性，并被组织成三个相互关联的主题。该项目的第一个重点是“稳健的联合学习”，它涉及开发多视图联合学习算法和编码的联合学习方法，以解决医疗保健环境中固有的统计和系统异构性挑战。该项目的第二个重点是“值得信赖的联合学习”，将开发新的联合对抗性训练和可验证的联合遗忘算法，以实现对对抗性攻击和数据删除请求的弹性，并将采用公平和可解释的算法，使拟议的联合学习解决方案对所有患者和利益相关者不偏不倚和公平。最后，在该项目的第三个主旨中，研究人员将研究和开发算法，为联邦学习生成和评估真实的合成健康数据，以实现重复性，并加速联邦学习方法在医疗保健中的发展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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 women, underrepresented 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.