Collaborative Research: MLWiNS: A Coding-Centric Approach to Robust, Secure, and Private Distributed Learning over Wireless

由深度神经网络（dnn）等机器学习（ML）和数十亿边缘计算设备（通过互联网基础设施连接传感器）组成的新兴生态系统（即物联网或IoT）驱动的人类和工业自动化正在塑造我们社会的未来。联邦学习（也称为协作学习）技术跨多个分散的边缘设备和/或保存本地数据样本的服务器工作，并通过交换参数（即与深度网络相关的权重）而不是实际的数据样本来促进算法的训练。由于与通信特性相关的数据丢失，无线网络上的联邦学习具有挑战性。该项目的目标是通过一个名为编码计算的创新框架，在无线边缘启用联邦学习，为他们提供急需的增强智能。在低成本边缘设备上普及机器学习的社会影响也将是巨大的。例如，在工作场所自动持续跟踪安全的智能边缘网络可以产生重大的社会和经济影响。该项目为这些应用程序的可扩展实现铺平了道路。编码计算在大规模分布式机器学习方面取得了巨大成功，人们可以明智地以编码方式创建计算冗余，以有效地处理通信瓶颈和系统干扰，如掉线、中断、节点故障和对抗性计算——这正是阻碍机器学习分布式无线边缘计算的一系列挑战。该项目引导了无线联合机器学习的理论和算法的发展，这些理论和算法是由编码和信息理论的基本原理驱动的。特别是，该项目全面解决了以下挑战：(i)无线带宽成本，（ii）无线中断弹性，（iii）安全性，以及（iv）优先考虑用户数据隐私，这对大规模用户参与无线边缘计算至关重要。无线网络的另一个关键方面是移动用户可以任意地加入和离开网络，并且用户的位置可以频繁变化。研究小组将开发一个联邦学习框架，通过设计一个自配置协议来适应这种动态网络拓扑，该协议可以适应新用户的移动，从而适应网络拓扑的变化。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。

Human and industrial automation, powered by machine learning (ML) such as Deep Neural Networks (DNNs) and the burgeoning ecosystem of billions of edge computing devices with sensors connected through the infrastructure of the internet (i.e., Internet of Things, or IoT) is shaping the future of our society. Federated learning (also known as, collaborative learning) techniques work across multiple decentralized edge devices and/or servers holding local data samples and facilitate training of the algorithms by exchanging parameters (i.e., weights associated with deep networks) instead of the actual data samples. Federated learning over wireless networks is challenging because of data loss associated with the communication characteristics. The goal of this project is to provide their critically needed augmented intelligence by enabling federated learning at the wireless edge, via an innovative framework, named coded computing. The societal impact of democratizing machine learning on low cost edge devices is also expected to be vast. For instance, smart edge networks that track safety automatically and continuously in workplaces can have a significant societal and economic impact. This project paves the path towards scalable realization of such applications. Coded computing has been hugely successful for large-scale distributed machine learning, where one can judiciously create computational redundancy in a coded manner to efficiently deal with communication bottleneck and system disturbances such as stragglers, outages, node failures, and adversarial computations -- precisely the set of challenges that hobble distributed wireless edge computations for machine learning. This project leads to the development of theory and algorithms for federated machine learning over wireless that are driven by fundamental principles informed by coding and information theory. In particular, this project holistically addresses the challenges of (i) wireless bandwidth costs, (ii) resiliency to wireless outages, (iii) security, and (iv) prioritizing user data privacy that is critical for large-scale user participation in wireless edge computing. Another key aspect of wireless networks is that mobile users join and leave the network arbitrarily, and user locations can change frequently. The research team will develop a federated learning framework that can adapt to such dynamic network topologies by designing a self-configurable protocol that can accommodate new users on-the-go, thereby adapting to the changes in the network topology.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.