MLWiNS: Collaborative Training and Inference at the Wireless Edge for Collective Intelligence

将数据、计算和控制转移到云中是过去十年的一个重要趋势。然而，物联网（IoT）正在产生前所未有的数据量和数据种类，因此，当数据进入云端进行分析和决策时，对其采取行动的机会可能已经消失。将数据计算和分析从云端移动到无线边缘，可以满足应用延迟要求，提高物联网设备的可扩展性和能效，并减轻网络上的流量负担。无线网络边缘的机器学习（ML），被称为边缘ML，强调利用本地计算的能力和使用边缘设备（例如，智能电话）作为边缘服务器来提供智能服务（例如，控制和决策）。然而，在网络边缘启用ML在模型训练/推理的联合设计以及延迟/隐私下的通信方面带来了新的基本挑战。虽然该项目旨在解决这些根本问题，但它有可能对基础科学、教育和技术产生更广泛的长期影响。它带来了计算，通信和机器学习的专业知识，以实现物联网中的人工智能。它还促进了行业之间的合作（例如，英特尔）和学术界，从而促进技术转让。分析大多数物联网数据的理想位置似乎是在产生和处理这些数据的设备附近。这需要分布式、低延迟和可靠的边缘ML。该项目的任务是在两个高级场景中使用无线边缘的可用数据进行ML，（i）联合学习，以及（ii）网络级智能的协作训练和推理。然而，必须解决几个挑战来支持边缘ML，特别是在考虑有限无线带宽上的实时ML时：（i）训练数据在大量边缘设备上分布不均匀。(ii)每个边缘设备都可以访问一小部分数据，因此不仅训练而且推理都必须协同进行。(iii)在网络上共享部分计算的模型或数据会引发隐私问题。因此，在Edge ML中，神经网络模型的设计、训练和推理都与无线通信和设备上的资源约束有关。该项目将利用随机线性编码、列线图函数表示和模拟联合源通道编码等技术来开发分布式机器学习框架，这些框架针对所需的边缘机器学习任务进行定制，并认识到边缘的限制。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。

Moving data, computation, and control into the cloud has been a significant trend in the past decade. However, the Internet of Things (IoT) is generating an unprecedented volume and variety of data and hence, by the time the data makes its way to the cloud for analysis and decision making, the opportunity to act on it might be gone. Moving the data computation and analytics from the cloud to the wireless edge enables the possibility of meeting application delay requirements, improves the scalability and energy efficiency of IoT devices, and mitigates the traffic burden on the network. Machine Learning (ML) at the wireless-network edge, referred to as edge ML, emphasizes leveraging the power of local computing and using edge devices (e.g., smartphones) as edge servers to provide intelligent services (e.g., control and decision making). However, enabling ML at the network edge introduces novel fundamental challenges in terms of the joint design of model training/inference, and communication under latency/privacy. Although the project is aimed at addressing these fundamental problems, it has the potential for long-term broader impacts on basic science, education, and technology. It brings expertise from computing, communication, and machine learning to enable artificial intelligence in the Internet of Things. It also fosters collaboration between industry (e.g., Intel) and academia and hence facilitates technology transfer. The ideal place to analyze most IoT data appears to be near the devices that produce and act on that data. This calls for distributed, low-latency, and reliable edge ML. The project is tasked with ML using available data at the wireless edge in two high-level scenarios, (i) federated learning, and (ii) collaborative training and inference for network-level intelligence. However, several challenges must be addressed to support edge ML, especially when considering real-time ML over limited wireless bandwidth: (i) Training data is unevenly distributed over a large number of edge devices. (ii) Every edge device has access to a tiny fraction of data and hence not only training but also inference must be carried out collaboratively. (iii) Sharing partially computed models or data across the network raises privacy issues. Therefore, in Edge ML, neural-network model design, training, and inference are entangled with both wireless communication and on-device resource constraints. This project will leverage techniques such as random linear coding, nomographic function representation, and analog joint source-channel coding to develop distributed ML frameworks that are tailored to the desired edge ML tasks, and cognizant of constraints at the edge.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.