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

协作研究：MLWiNS：一种以编码为中心的方法，通过无线实现稳健、安全和私密的分布式学习

基本信息

批准号：
2002821
负责人：
Kannan Ramchandran
金额：
$ 6.67万
依托单位：
University of California-Berkeley
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002821&HistoricalAwards=false
关键词：
Collaborative Research MLWiNS Coding Centric

项目摘要

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.

人类和工业自动化由深度神经网络 (DNN) 等机器学习 (ML) 以及由数十亿个边缘计算设备组成的新兴生态系统提供支持，这些设备带有通过互联网基础设施（即物联网或 IoT）连接的传感器。塑造我们社会的未来。联合学习（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，无线网络上的联合学习具有挑战性。该项目的目标是通过名为编码计算的创新框架在无线边缘实现联合学习，从而提供他们急需的增强智能。低成本边缘设备上的机器学习民主化预计也会产生巨大的社会影响。例如，自动持续跟踪工作场所安全的智能边缘网络可以产生重大的社会和经济影响。该项目为此类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，人们可以以编码方式明智地创建计算冗余，以有效地处理通信瓶颈和系统干扰，例如掉队、中断、节点故障和对抗性计算——恰恰是一系列阻碍机器学习分布式无线边缘计算的挑战。该项目导致了无线联合机器学习理论和算法的发展，这些理论和算法是由编码和信息论所告知的基本原理驱动的。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，这对于大规模用户参与无线边缘至关重要计算。无线网络的另一个关键方面是移动用户可以任意加入和离开网络，并且用户位置可能会频繁变化。研究团队将开发一种能够适应这种动态网络拓扑的联邦学习框架，通过设计一种可自配置的协议，可以容纳移动中的新用户，从而适应网络拓扑的变化。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。