CRII: III: Towards Improving the Handling of Heterogeneity and Personalization in Federated Learning

CRII：III：改进联邦学习中异构性和个性化的处理

• 批准号: 2246067
• 负责人: Lichao Sun
• 金额: $ 17.5万
• 依托单位: Lehigh University
• 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: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246067&HistoricalAwards=false
• 关键词: CRII; III; Towards; Improving; Handling

As awareness of the need for privacy preservation continues to grow in society, new legal restrictions, such as the General Data Protection Regulation (GDPR), are emerging. Such laws demand that businesses and organizations not share their clients' raw data for any commercial purposes. Federated Learning (FL) is a distributed machine learning paradigm that works with decentralized data while preserving privacy. FL has gained widespread interest and has been applied in numerous applications, such as healthcare, education, and intelligent manufacturing. However, FL faces some challenges that come from, executing on diverse types of data and devices, such as mobile phones and Internet of Things (IoT) devices. This project aims to address the aforementioned issues in heterogeneous FL by developing mathematical models and efficient algorithms. In addition, the project will integrate trustworthy ML research into new curriculum development and support students from underrepresented groups.Heterogeneous FL faces two significant challenges: (1) each client in FL may generate data according to a distinct distribution; (2) heterogeneous clients, such as mobile phones and IoT devices, are equipped with a wide range of computation and communication capabilities. To address these challenges, this project will dramatically push the boundary of knowledge via the following two integrated research thrusts: (i) The research team aims to tackle data heterogeneity in FL by designing two advanced personalized learning methods. Specifically, the proposed solutions aim to balance the generalization ability from the global model and the personalization ability from the local model, improving both the global model and personalized local models. (ii) The team will study heterogeneous neural network aggregation for FL by providing advanced memory-efficient local training strategies for small devices. In addition, the project will make use of mutual knowledge distillation to improve the generalization ability of the local models. Finally, the team proposes a unified FL framework that integrates data-free knowledge aggregation with advanced memory-efficient solutions to tackle both heterogeneity issues simultaneously.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.

随着对隐私保护需求的认识在社会中不断增长，新的法律限制（例如一般数据保护法规（GDPR））正在出现。这样的法律要求企业和组织不会出于任何商业目的共享客户的原始数据。联合学习（FL）是一个分布式的机器学习范式，可在保留隐私的同时与分散数据一起使用。佛罗里达州已获得广泛的兴趣，并已应用于许多应用程序，例如医疗保健，教育和智能制造业。但是，FL面临着一些挑战，即在各种类型的数据和设备（例如手机和物联网（IoT）设备）上执行。该项目旨在通过开发数学模型和有效算法来解决异质FL中上述问题。此外，该项目将将值得信赖的ML研究集成到新的课程开发中，并支持来自代表性不足的组的学生。H -HEDENECEELOUS FL面临两个重大挑战：（1）FL中的每个客户可能会根据独特的分布来生成数据； （2）异构客户，例如手机和物联网设备，配备了广泛的计算和通信功能。为了应对这些挑战，该项目将通过以下两个集成的研究推力大大推动知识的边界：（i）研究小组旨在通过设计两种先进的个性化学习方法来解决FL中的数据异质性。具体而言，拟议的解决方案旨在平衡全球模型的概括能力以及本地模型的个性化能力，从而改善了全球模型和个性化的本地模型。 （ii）团队将通过为小型设备提供高级记忆有效的本地培训策略来研究FL的异质神经网络聚合。此外，该项目将利用相互知识蒸馏来提高本地模型的概括能力。最后，该团队提出了一个统一的FL框架，将无数据知识汇总与高级记忆有效的解决方案集成在一起，以同时解决这两个异质性问题。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来支持的。