Collaborative Research: CNS Core: Small: Hierarchical Federated Learning Over Wireless Edge Networks: Performance Analysis and Optimization

协作研究：CNS 核心：小型：无线边缘网络的分层联邦学习：性能分析和优化

• 批准号: 2114267
• 负责人: Walid Saad
• 金额: $ 16.52万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114267&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

Federated learning (FL) is revolutionizing machine learning by catalyzing a paradigm shift from cloud-based centralized learning towards distributed, on-device edge learning. FL enables devices to collaboratively train and execute a global learning task by using local processing and simple learning parameters exchange, thus avoiding the communication and privacy concerns associated with sharing large data volumes with a remote cloud. Owing to its attractive privacy, scalability, and communication features, FL will be an integral edge component of Internet of Things (IoT) services such as autonomous systems. However, when deployed over the wireless IoT edge, the performance of FL will be largely constrained by the quality of the wireless links used to exchange the local and global FL model parameters. Since the next-generation IoT will be powered by a wireless cellular system (e.g., 5G), reaping the benefits of FL for the IoT hinges on understanding how wireless factors, such as fading, interference, and delay, impact the convergence and performance of FL (e.g., accuracy, reliability, and convergence time). The goal of this research is to develop a foundational framework that rigorously answers fundamental questions on the achievable FL performance over realistic, large-scale wireless edge networks thus facilitating FL integration unto a real-world IoT. The research is coupled with a well-crafted educational plan that includes a new course at the intersection of communications and machine learning as well as a significant involvement of graduate and undergraduate students at all levels. Broad dissemination and outreach will be ensured via several workshops, tutorials, outreach events, and other tools.This research will develop a novel, holistic framework for performance analysis and optimization of FL over large-scale wireless cellular edge networks. The proposed framework will yield major innovations across both wireless and FL fields: 1) A scalable hierarchical wireless architecture that allows a large-scale implementation of FL over wireless cellular systems, 2) Rigorous performance analysis of hierarchical FL over wireless edge networks that will yield novel FL performance metrics that jointly couple learning performance indicators, such as training accuracy and convergence time, 3) Novel notions of reliability for FL over wireless networks to enable the operation of FL under extreme network conditions and in presence of IoT device mobility and spatio-temporal correlations, and 4) Suitable resource allocation algorithms that can optimize the performance of hierarchical FL over wireless edge networks. The results will be validated using various simulation and experimental means.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.

联邦学习（FL）正在通过催化从基于云的集中学习向分布式的，设备的边缘学习的范式转变来彻底改变机器学习。 FL使设备可以通过使用本地处理和简单的学习参数交换来协作训练和执行全局学习任务，从而避免与与远程云共享大数据量相关的通信和隐私问题。由于其吸引人的隐私，可扩展性和通信功能，FL将成为物联网（IoT）服务（例如自主系统）的整体边缘组成部分。但是，当在无线物联网边缘部署时，FL的性能将在很大程度上受到用于交换本地和全局FL模型参数的无线链接的质量。由于下一代物联网将由无线蜂窝系统（例如5G）提供动力，因此从IoT链接中获得了FL的益处，以了解了解无线因素（例如褪色，干扰和延迟）如何影响FL的融合和性能（例如，准确性，可靠性，可靠性和收敛时间）。这项研究的目的是开发一个基本框架，该框架严格回答有关现实，大规模无线边缘网络可实现的FL性能的基本问题，从而促进FL集成到现实世界的物联网。这项研究与精心制作的教育计划相结合，其中包括在通信和机器学习交集的新课程，以及各级研究生和本科生的重要参与。将通过多个研讨会，教程，外展事件和其他工具来确保广泛的传播和外展。这项研究将开发出一个新颖的，整体的绩效框架，用于性能分析和在大型无线蜂窝边缘网络上进行FL的优化。 The proposed framework will yield major innovations across both wireless and FL fields: 1) A scalable hierarchical wireless architecture that allows a large-scale implementation of FL over wireless cellular systems, 2) Rigorous performance analysis of hierarchical FL over wireless edge networks that will yield novel FL performance metrics that jointly couple learning performance indicators, such as training accuracy and convergence time, 3) Novel notions of reliability for FL over wireless networks to enable the operation在极端网络条件下的FL以及物联网设备移动性和时空相关性的存在，以及4）适当的资源分配算法，这些算法可以优化无线边缘网络层次级别的性能。该奖项将通过各种模拟和实验手段进行验证。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论标准来评估值得支持的。

项目成果

On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks

• DOI: 10.1109/icc45855.2022.9838362
• 发表时间: 2021-11
• 期刊: ICC 2022 - IEEE International Conference on Communications
• 作者: Minsu Kim;W. Saad;Mohammad Mozaffari;M. Debbah

Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks

• DOI: 10.1109/twc.2023.3297790
• 发表时间: 2024-03
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Sihua Wang;Mingzhe Chen;Christopher G. Brinton;Changchuan Yin;W. Saad;Shuguang Cui

Vector Quantized Compressed Sensing for Communication-Efficient Federated Learning

• DOI: 10.1109/gcwkshps56602.2022.10008615
• 发表时间: 2022-12
• 期刊: 2022 IEEE Globecom Workshops (GC Wkshps)
• 作者: Yong-Nam Oh;Yo-Seb Jeon;Mingzhe Chen;W. Saad

Green, Quantized Federated Learning Over Wireless Networks: An Energy-Efficient Design

• DOI: 10.1109/twc.2023.3289177
• 发表时间: 2022-07
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Minsu Kim;W. Saad;Mohammad Mozaffari;M. Debbah

Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

• DOI: 10.1109/globecom48099.2022.10001466
• 发表时间: 2022-12
• 期刊: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
• 作者: Nuocheng Yang;Sihua Wang;Mingzhe Chen;Christopher G. Brinton;Changchuan Yin;W. Saad;Shuguang Cui