Collaborative Research: SaTC: CORE: Medium: PREMED: Privacy-Preserving and Robust Computational Phenotyping using Multisite EHR Data

合作研究：SaTC：核心：中：PREMED：使用多站点 EHR 数据的隐私保护和鲁棒计算表型分析

• 批准号: 2124104
• 负责人: Li Xiong
• 金额: $ 90万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124104&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Medium

Tensor analysis offers an effective approach to convert massive Electronic Health Records (EHRs) into meaningful and interpretable clinical concepts, or phenotypes, such as diseases and disease subtypes. It can cluster patients into subgroups and capture the interactions between multiple attributes (e.g., specific procedures used to treat a disease), enabling precision medicine. Effective phenotyping needs to be supported by a large number of diverse samples to avoid potential population bias. A major challenge is how to derive phenotypes jointly across multiple institutions, while preserving individual patients' privacy at each site. The goal of this project is to develop a federated tensor factorization framework for Privacy-preserving, Robust, and Efficient computational phenotyping using Multisite EHR Data (PREMED). While many techniques have been developed for federated learning for each of these goals, their synergy has not been well studied. Communication-efficient techniques such as compression have an intrinsic benefit to privacy (smaller disclosure risks) and robustness (smaller adversarial impact) due to the compressed and obfuscated communication. Further, federated tensor factorization presents unique challenges due to its multi-factor structure and unsupervised nature. The project aims to exploit the synergy between efficiency, privacy, and robustness and address the three interrelated challenges with a holistic approach, while utilizing the multi-factor structure of tensor factorization. The research outcome will allow institutions to jointly perform computational phenotyping using their privacy-protected data effectively and efficiently. This project includes a set of interrelated objectives including: (1) developing communication-efficient techniques for federated tensor factorization such as local Stochastic Gradient Descent (SGD) to reduce communication frequency; and multi-level compression methods to reduce per-round communication leveraging the multi-factor structure of tensor factorization; (2) developing privacy-preserving federated tensor factorization methods by exploiting the intrinsic privacy benefit of the communication-efficient techniques; and privacy-preserving input synthesization methods that offer more versatility; and (3) developing robust statistical aggregation methods for handling potential Byzantine failures and malicious sites by utilizing the intrinsic robustness benefit of the communication-efficient techniques; and robust learning-based aggregation methods for sparse settings based on truth inference and adaptive site valuation approaches. The project includes case studies using real EHR data from Emory and UTHealth for phenotype discovery and phenotype-based predictive studies in the context of Alzheimer's Disease and Sepsis. The project also includes a set of synergistic activities including organization of multi-site computational phenotyping challenges; development of collaborative sidecar courses; and active involvement of undergraduates, women and underrepresented groups.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.

张量分析提供了一种有效方法，可将大量电子健康记录 (EHR) 转换为有意义且可解释的临床概念或表型，例如疾病和疾病亚型。它可以将患者分为亚组并捕获多个属性之间的相互作用（例如用于治疗疾病的特定程序），从而实现精准医疗。有效的表型分析需要大量不同样本的支持，以避免潜在的群体偏差。一个主要的挑战是如何跨多个机构联合得出表型，同时保护每个站点的个体患者的隐私。该项目的目标是使用多站点 EHR 数据 (PREMED) 开发一个联合张量分解框架，用于保护隐私、稳健且高效的计算表型。虽然针对这些目标中的每一个目标都开发了许多联邦学习技术，但它们的协同作用尚未得到充分研究。由于压缩和混淆的通信，诸如压缩之类的通信高效技术对隐私（较小的泄露风险）和鲁棒性（较小的对抗性影响）具有内在的好处。此外，联合张量分解由于其多因素结构和无监督性质而提出了独特的挑战。该项目旨在利用效率、隐私和鲁棒性之间的协同作用，并通过整体方法解决三个相互关联的挑战，同时利用张量分解的多因素结构。研究成果将使各机构能够利用其受隐私保护的数据有效且高效地联合执行计算表型分析。该项目包括一系列相互关联的目标，包括：（1）开发用于联合张量分解的通信高效技术，例如局部随机梯度下降（SGD）以降低通信频率；以及多级压缩方法，利用张量分解的多因子结构来减少每轮通信； （2）通过利用通信高效技术的内在隐私优势，开发保护隐私的联合张量分解方法；以及提供更多多功能性的保护隐私的输入合成方法； (3) 利用通信高效技术固有的鲁棒性优势，开发鲁棒的统计聚合方法来处理潜在的拜占庭故障和恶意站点；以及基于真值推理和自适应站点评估方法的稀疏设置的基于学习的稳健聚合方法。该项目包括使用埃默里大学和 UTHealth 的真实 EHR 数据进行案例研究，用于阿尔茨海默病和脓毒症的表型发现和基于表型的预测研究。该项目还包括一系列协同活动，包括组织多位点计算表型挑战；开发协作边车课程；该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Personalized Differentially Private Federated Learning without Exposing Privacy Budgets

• DOI: 10.1145/3583780.3615247
• 发表时间: 2023-10
• 期刊: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
• 作者: Junxu Liu;Jian Lou;Li Xiong;Xiaofeng Meng

Projected Federated Averaging with Heterogeneous Differential Privacy

• DOI: 10.14778/3503585.3503592
• 发表时间: 2021-12
• 期刊: Proc. VLDB Endow.
• 作者: Junxu Liu;Jian Lou;Li Xiong;Jinfei Liu;Xiaofeng Meng

PubMed-OA-Extraction-dataset

PubMed-OA-提取数据集

• DOI: 10.5281/zenodo.6330817
• 发表时间: 2022
• 期刊: Zenodo
• 作者: Sheng, Jiasheng

MUter: Machine Unlearning on Adversarial Training Models

MUter：对抗性训练模型的机器遗忘

• 发表时间: 2023
• 期刊: International Conference on Computer Vision
• 作者: Liu, Junxu;Xue Mingsheng;Lou Jian;Zhang, Xiaoyu;Xiong, Li;Qin, Zhan
• 通讯作者: Qin, Zhan

Federated Pruning: Improving Neural Network Efficiency with Federated Learning

• DOI: 10.21437/interspeech.2022-10787
• 发表时间: 2022-09
• 作者: Rongmei Lin;Yonghui Xiao;Tien-Ju Yang;Ding Zhao;Li Xiong;Giovanni Motta;Franccoise Beaufays