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

联邦学习（FL）是一种分布式机器学习方法，它允许多个数据所有者（“客户端”）协作训练机器学习模型，这些模型可以从每个所有者的数据中受益，而无需共享数据本身。联邦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行业的一个有吸引力的工具。然而，联邦学习受制于关键学习（CL）时期：最初的几轮训练对模型的质量和鲁棒性有巨大的影响。如果可以开发用于选择和加权来自不同客户的贡献的新方法来解决CL周期的原因，则CL周期可能有助于联邦学习系统提高模型质量。然而，它们也为攻击者提供了机会，攻击者可以利用CL周期发起更精确、更有影响力的攻击。为了更好地了解这些机会和攻击，本项目将对CL期的特征和可利用性进行全面分析，从而推进联邦学习的鲁棒性和脆弱性研究。该团队将开发数据集、模型、算法和系统源代码，并与研究界共享，而科学发现将以研究论文、技术报告、书籍章节、课程材料和教程的形式广泛传播。本科生，特别是来自代表性不足群体的本科生，将参与拟议的研究活动。这个项目的中心目标是在FL训练过程中调查和理解CL周期，利用CL周期的独特属性来增强FL的安全性和健壮性，同时发现攻击者可能利用的漏洞。为了实现这一目标，该项目研究了三个主要主题。第一个主题侧重于如何在FL培训期间及时有效地识别CL期和相关漏洞。第二个主题侧重于如何通过CL期意识来优化FL模型的准确性，重点是针对第一个主题中开发的CL期原因进行自适应客户选择的方法。第三个主题探讨了将主题1的发现推广到其他流行的FL技术的方法，如梯度压缩、公平聚合、个性化及其联合效应，以解决系统异质性（例如，通信带宽差异、异构本地模型和公平性问题）。在三个主题的同时，该团队还将设计和开发一个强大的FL测试平台，以实际模型和数据集对提出的算法进行实证评估。该项目由安全和可信网络空间和促进竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.