CAREER: Foundations of Federated Multi-Task Learning

职业：联合多任务学习的基础

基本信息

批准号：
2145670
负责人：
Virginia Smith
金额：
$ 59.72万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2027-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145670&HistoricalAwards=false
关键词：
CAREER Foundations Federated Multi Task

项目摘要

Mobile phones, wearable devices, and smart homes form just a few of the modern distributed networks generating a wealth of data each day. Due to the growing computational power of edge devices, coupled with concerns over transmitting private data, it is increasingly attractive to store data locally and push network computation to the edge. Federated learning explores training machine learning models at the edge in distributed networks. While federated learning has shown tremendous promise for enabling edge applications, practical deployment is currently stymied by a number of competing constraints. In addition to being accurate, federated learning methods must scale to potentially massive networks of devices, and must exhibit trustworthy behavior---addressing pragmatic concerns related to issues such as user privacy, fairness, and robustness. In this project, we explore multi-task learning, a technique that learns separate but related models for each device in the network, as a unified approach to address the competing constraints of federated learning. The objective of the project is to develop scalable multi-task learning methods that are suitable for practical federated networks, and to rigorously study the foundational properties of federated multi-task learning in terms of the goals of accuracy, scalability, and trustworthiness. In doing so, the research will unlock a new generation of federated learning systems that can holistically address the constraints of realistic federated networks.The goal of this project is to establish and rigorously study the use of federated multi-task learning. While the accuracy benefits of federated multi-task learning are well-known, the work charts two new directions. First, the project develops methods to realize multi-task learning at scale in massive federated networks. Secondly, the project shows that multi-task learning, by improving privacy, fairness, and robustness, is in fact key for trustworthy federated learning. The technical aims of the project work are divided into three thrusts. First, by approximating standard notions of multi-task learning, the project will develop and rigorously study a family of highly scalable federated multi-task learning objectives. Second, the privacy implications of multi-task learning will be analyzed and evaluated in order to understand trade-offs between privacy and utility in federated networks. Finally, this project will explore tensions between fairness (in terms of performance disparities across devices) and robustness (to data and model poisoning attacks) in federated learning. Although these goals may be at odds, this project aims to show that multi-task learning can inherently improve both fairness and robustness, helping both to be achieved jointly. Taken together, this work has the potential to cause a paradigm-shift in the way federated learning systems are designed, implemented, and analyzed.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.

手机，可穿戴设备和智能家居仅形成了几个现代分布式网络，每天都会产生大量数据。由于边缘设备的计算能力不断增长，再加上对传输私有数据的担忧，因此在本地存储数据并将网络计算推向边缘越来越有吸引力。联合学习探索了分布式网络边缘的培训机学习模型。尽管联邦学习对实现边缘应用程序表现出了巨大的希望，但实际部署目前受到许多竞争性约束的困扰。除了准确之外，联邦学习方法还必须扩展到潜在的大量设备网络，并且必须表现出可信赖的行为 - - 解决与用户隐私，公平和鲁棒性等问题有关的实用问题。在这个项目中，我们探讨了多任务学习，该技术可以学习网络中每个设备的单独但相关模型，作为解决联合学习的竞争约束的统一方法。该项目的目的是开发适合实用联合网络的可扩展多任务学习方法，并以准确性，可伸缩性和可信赖性的目标来严格研究联邦多任务学习的基础性能。通过这样做，研究将解锁新一代的联合学习系统，这些系统可以整体解决现实的联邦网络的限制。该项目的目的是建立和严格地研究联邦多任务学习的使用。尽管联合多任务学习的准确性优势是众所周知的，但工作却显示了两个新的方向。首先，该项目开发了在大规模联邦网络中实现多任务学习的方法。其次，该项目表明，通过改善隐私，公平和鲁棒性，多任务学习实际上是值得信赖的联合学习的关键。项目工作的技术目标分为三个推力。首先，通过近似多任务学习的标准概念，该项目将开发并严格研究一个高度可扩展的联邦多任务学习目标的家庭。其次，将分析和评估多任务学习的隐私含义，以了解联合网络中隐私和公用事业之间的权衡。最后，该项目将探讨联邦学习中公平性（就设备之间的性能差异而言）与鲁棒性（数据和模型中毒攻击）之间的紧张关系。尽管这些目标可能是矛盾的，但该项目的目的是表明多任务学习可以固有地改善公平和稳健性，从而有助于共同实现。综上所述，这项工作有可能在设计，实施和分析联合学习系统的方式中引起范式偏移。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来评估的审查标准。