CIF: Small: Efficient and Secure Federated Structure Learning from Bad Data

CIF：小型：高效、安全的联邦结构从不良数据中学习

• 批准号: 2341359
• 负责人: Namrata Vaswani
• 金额: $ 60万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341359&HistoricalAwards=false
• 关键词: CIF; Small; Efficient; Secure; Federated

This project develops secure distributed algorithms for efficiently solving a large class of optimization problems that occur in medical imaging and machine learning. Important examples include accelerated magnetic resonance imaging (MRI), product recommender systems, computer vision (e.g., occlusion removal or video editing), and bioinformatics (grouping of unlabeled data). The focus is on algorithms that are fast, require communicating only small amounts of data, and work well in the data-scarce regime. Algorithm speed is an important concern in all modern applications. Within MRI, it is essential for (near) real-time applications such as interventional MRI or on the fly identification and correction of artifacts, e.g., re-scanning if the patient coughs during the first scan. Sample efficiency is critical for accelerating the MRI scan, or for learning user ratings of products from very few available ones. The project also supports Early Math education via the CyMath program, a program in which Math-loving graduate students provide after-school Math tutoring support for students as young as third graders. This project introduces a novel solution framework called alternating gradient descent (GD) and minimization that provides a faster and more communication-efficient solution for many optimization problems for which alternating minimization (AltMin) is a popular solution. In particular, it is useful for any problem for which the minimization over one set of variables is much quicker than that over the other set. Starting with a careful initialization for one set, AltGDmin alternately updates the variables using minimization for the quicker set and gradient descent (GD) for the other set. Often, the reason that the minimization is fast over some variables is that the optimization problem is decoupled with respect to these variables. This decoupling also helps guarantee per-iteration communication-efficiency and privacy in federated settings. The use of minimization for one set of the variables is also what helps ensure sufficient error decay in each algorithm iteration. This implies that, for certain problems such as low rank column-wise sensing, AltGDmin is almost as fast and as communication-efficient per iteration as (factorized) GD, while converging almost as quickly as AltMin. This makes it faster overall than both types of solutions. Problem-specific correctness guarantees are derived. These determine the theoretical bounds on the iteration complexity and the sample complexity. Obtaining these results requires the development of novel proof techniques that may be of independent interest. The reason is AltGDmin is neither an AltMin approach nor a a standard GD algorithm for any subset of variables. The design and analysis Byzantine resilient (secure) AltGDmin algorithms is being studied for various low rank, and other structure, recovery problems.This project is jointly funded by the Computing and Communications Foundations (CCF) division of the Computer and Information Sciences Directorate (CISE) and the Established Program to Stimulate Competitive Research (EPSCoR).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.

该项目开发了安全的分布式算法，以有效地解决医学成像和机器学习中发生的大量优化问题。重要的例子包括加速磁共振成像（MRI），产品推荐系统，计算机视觉（例如，遮挡删除或视频编辑）和生物信息学（未标记数据的分组）。 重点是快速的算法，仅需要传达少量数据，并且在数据筛选方面效果很好。 在所有现代应用中，算法速度是一个重要问题。在MRI中，对于（近）实时应用，例如介入的MRI或Fly识别和校正工件，例如，如果患者在第一次扫描中咳嗽，则至关重要。 样品效率对于加速MRI扫描或从很少有可用产品中学习用户评分至关重要。该项目还通过Cymath计划支持早期的数学教育，该计划在该计划中，喜欢数学的研究生为小学生提供了小学数学辅导支持，为年龄较小的三年级学生提供支持。该项目引入了一个新的解决方案框架，称为交流梯度下降（GD），并最小化，该框架为许多优化问题提供了更快，更具通信的效率解决方案，以最小化（Altmin）是一种流行的解决方案。特别是，对于任何问题，一组变量的最小化比另一组方面的最小化要快得多。从一组仔细的初始化开始，Altgdmin使用最小化的更快集和梯度下降（GD）交替更新变量。通常，最小化在某些变量上很快的原因是优化问题是相对于这些变量的。这种解耦还有助于保证联合设置中的触电沟通效率和隐私。最小化对一组变量的使用也是有助于确保每种算法迭代中足够的错误衰减的原因。这意味着，对于某些问题，例如低等级柱的传感，Altgdmin几乎与（分解）GD一样快，沟通效率高，同时收敛的速度几乎与Altmin一样快。这使得整体上比两种类型的解决方案都要快。得出了特定问题的正确性保证。这些决定了迭代复杂性和样品复杂性的理论界限。获得这些结果需要开发可能具有独立关注的新型证明技术。原因是Altgdmin既不是Altmin方法，也不是任何变量子集的标准GD算法。 设计和分析拜占庭式弹性（安全）Altgdmin算法正在研究各种低级和其他结构，恢复问题。该项目由计算机和通信基金会（CCF）共同资助。计算机和信息科学局（CCF）部门（CCF）部门（CCF）分区（CISE）（CISE）（CISE）（CISE）（CISE）和建立的计划，以刺激有竞争力的研究（EPSCOR）。使用基金会的智力优点和更广泛的影响评估标准进行评估。