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

联邦学习（FL）通过催化从基于云的集中式学习向分布式设备边缘学习的范式转变，正在彻底改变机器学习。FL使设备能够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，从而避免了与远程云共享大数据量相关的通信和隐私问题。由于其具有吸引力的隐私性、可扩展性和通信特性，FL将成为自动系统等物联网（IoT）服务不可或缺的边缘组件。然而，当部署在无线物联网边缘时，FL的性能将在很大程度上受到用于交换本地和全局FL模型参数的无线链路质量的限制。由于下一代物联网将由无线蜂窝系统（例如5G）供电，因此为物联网获得FL的好处取决于了解无线因素（例如衰落，干扰和延迟）如何影响FL的收敛和性能（例如精度，可靠性和收敛时间）。本研究的目标是开发一个基础框架，严格回答有关在现实的大规模无线边缘网络上可实现的FL性能的基本问题，从而促进FL集成到现实世界的物联网。该研究与精心设计的教育计划相结合，其中包括通信和机器学习交叉的新课程，以及各级研究生和本科生的大量参与。将通过若干讲习班、教程、外展活动和其他工具确保广泛传播和外展。本研究将开发一个全新的整体框架，用于大规模无线蜂窝边缘网络上的FL性能分析和优化。拟议的框架将在无线和FL领域产生重大创新：1)可扩展的分层无线架构，允许在无线蜂窝系统上大规模实施FL； 2)对无线边缘网络上的分层FL进行严格的性能分析，将产生新的FL性能指标，这些指标将联合耦合学习性能指标，如训练精度和收敛时间；3)新颖的无线网络FL可靠性概念，使FL能够在极端网络条件下以及物联网设备移动性和时空相关性的存在下运行；4)合适的资源分配算法，可以优化无线边缘网络分层FL的性能。结果将通过各种模拟和实验手段进行验证。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 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.