Collaborative Research: SaTC: CORE: Small: Critical Learning Periods Augmented Robust Federated Learning

协作研究：SaTC：核心：小型：关键学习期增强鲁棒联邦学习

• 批准号: 2315614
• 负责人: Jian Li
• 金额: $ 17万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2315614&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

Federated Learning (FL) is a distributed machine learning approach that allows multiple data owners ("clients") to collaboratively train machine learning models that benefit from each owner's data without having to share the data itself. Federated learning can improve privacy and protect restricted data, which makes it an attractive tool in sectors such as healthcare, fintech, and autonomous driving. However, federated learning is subject to critical learning (CL) periods: the initial rounds of training have an outsized impact on models' quality and robustness. CL periods may help federated learning systems improve model quality, if new methods for selecting and weighting contributions from different clients can be developed to address the causes of CL periods. However, they also present opportunities for attackers, who may be able to harness CL periods to launch more precise and impactful attacks. To better understand these opportunities and attacks, this project will conduct a comprehensive analysis of the characteristics and exploitability of CL periods so as to advance the study of the robustness and vulnerability of federated learning. The team will develop datasets, models, algorithms, and system source code and share it with the research community, while the scientific findings will be widely disseminated as research papers, technical reports, book chapters, course materials, and tutorials. Undergraduate students, particularly those from under-represented groups, will be engaged in the proposed research activities. The central goal of this project is to investigate and understand CL periods during the FL training process, exploiting unique properties of CL periods to enhance FL security and robustness while uncovering vulnerabilities that attackers could exploit. To achieve this objective, the project investigates three main themes. The first theme focuses on how to efficiently identify CL periods and related vulnerabilities in a timely manner during FL training. The second theme focuses on how to optimize FL model accuracy with CL periods awareness, focusing on methods for adaptive client selection that are tuned to the causes of CL periods developed in the first theme. The third theme investigates ways to generalize the findings from Theme 1 to other popular FL techniques such as gradient compression, fair aggregation, personalization, and their joint effect, to address system heterogeneity (e.g., communication bandwidth differences, heterogeneous local models, and fairness concerns). Concurrently with the three main themes, the team will also design and develop a robust FL testbed to empirically evaluate the proposed algorithms with real-world models and datasets.This project is jointly funded by Secure and Trustworthy Cyberspace 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.

联合学习（FL）是一种分布式机器学习方法，允许多个数据所有者（“客户”）协作训练机器学习模型，这些模型从每个所有者的数据中受益，而无需共享数据本身。联合学习可以改善隐私并保护受限制的数据，这使其成为医疗保健，金融科技和自动驾驶等领域中有吸引力的工具。但是，联邦学习需要批判性学习（CL）时期：最初的培训一轮对模型的质量和鲁棒性产生了巨大影响。如果可以开发出用于解决CL期间的原因的新方法，则CL期间可能有助于联合学习系统提高模型质量，如果可以开发出不同客户的加权贡献的新方法。但是，他们还为攻击者提供了机会，攻击者可能能够利用CL时期发起更精确和有影响力的攻击。为了更好地了解这些机会和攻击，该项目将对CL期间的特征和可剥削性进行全面分析，以提高研究联合学习的鲁棒性和脆弱性。该团队将开发数据集，模型，算法和系统源代码，并与研究社区共享，而科学发现将被广泛传播为研究论文，技术报告，书籍章节，课程材料和教程。本科生，特别是来自代表性不足的团体的学生，将从事拟议的研究活动。该项目的核心目标是在FL培训过程中调查和理解CL时期，利用CL期间的独特性能，以增强FL的安全性和鲁棒性，同时发现攻击者可能利用的脆弱性。为了实现这一目标，该项目调查了三个主要主题。第一个主题重点是如何在FL培训期间及时及时确定CL期间和相关漏洞。第二个主题重点是如何通过CL期间意识优化FL模型的精度，重点是自适应客户选择的方法，这些方法被调整为第一个主题中开发的CL时期的原因。第三个主题研究了将发现从主题1推广到其他流行的FL技术的方法，例如梯度压缩，公平聚集，个性化及其共同效果，以解决系统异质性（例如，通信带宽差异，异质本地模型和公平性问题）。该团队同时与三个主要主题一起设计和开发了一个强大的FL测试，以经验评估所提出的算法，该算法通过现实世界的模型和数据集进行了共同资助。该项目由安全且可信赖的网络空间和既定的计划以及既定的计划进行了支持，以刺激NSF的Internation Internation the Internitory of Altimatie the Interial the Interial the Interial the Interniqual ofertial ofertial the Internitial。和更广泛的影响审查标准。