CAREER: Wireless InferNets: Enabling Collaborative Machine Learning Inference on the Network Path

机器学习越来越多地被集成到无线网络应用中，如视频监控、智能医疗和工业物联网，以从无线设备收集或生成的丰富数据中获取可操作的智能。为了支持这些应用中的实时机器学习和决策，必须将大量数据从数据源传输到目的地，并由推理算法及时处理。传统上，数据传输和机器学习推理被视为两个独立的优化任务，但这种方法效率低下，因为它们忽略了数据传输和机器学习推理之间的相互作用，导致大的数据传输延迟或大的推理延迟。这个CAREER项目通过提出无线Infernets克服了现有方法的局限性，无线Infernets是一种新的无线网络架构，能够在数据传输路径上的网络节点之间进行协作机器学习推理。这个CAREER项目的成功完成将促进对分布式推理和网络之间协同作用的理解，并促进未来无线网络的范式转变，以支持安全、医疗保健和其他技术领域中的新兴应用和服务。该项目还包含一个重要的教育组成部分，并提供大量机会，培养和吸引学生，特别是来自代表性不足的群体的学生，从事计算机科学和工程。该CAREER项目开发模型，算法和协议，以实现无线Infernets的核心功能，并通过三个主要研究目标来解决网络和设备异构性，动态和不完善的网络状态以及多用户争夺有限资源所带来的挑战。具体而言，它的目标是（i）开发实用的分布式推理路由算法和协议，并进行理论分析，以了解性能限制：（ii）开发新的多臂强盗算法，以执行不确定网络信息的推理路由;（iii）开发分布式多智能体深度强化学习算法，用于多智能体中的推理路由，该奖项反映了NSF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。

Machine learning is increasingly being integrated into wireless network applications, such as video surveillance, smart healthcare and industrial internet of things, to derive actionable intelligence from the rich data collected or generated by wireless devices. To support real-time machine learning and decision making in these applications, a large amount of data has to be transferred from the data source to the destination and processed by an inference algorithm in a timely manner. Traditionally, data transfer and machine learning inference are treated as two separate optimization tasks, but such approaches are inefficient as they ignored the interaction between data transfer and machine learning inference, resulting in either a large data transfer delay or a large inference delay. This CAREER project overcomes the limitations of existing approaches by proposing wireless InferNets, a new wireless network architecture that enables collaborative machine learning inference among network nodes on the data transfer path. The successful completion of this CAREER project will promote the understanding of the synergy between distributed inference and networking, and catalyze a paradigm shift of future wireless networks to support emerging applications and services in security, healthcare and other technological domains. The project also contains a significant educational component and provides abundant opportunities to nurture and attract students, especially from underrepresented groups, to engage in computer science and engineering. This CAREER project develops models, algorithms and protocols to realize the core functions of wireless InferNets and address challenges caused by network and device heterogeneity, dynamic and imperfect network states, and multiple user contention for the limited resources via three main research aims. Specifically, it aims at (i) developing practical distributed inference routing algorithms and protocols and conducting theoretical analysis to understand the performance limits; (ii) developing new multi-armed bandit algorithms to perform inference routing with uncertain network information; (iii) developing distributed multi-agent deep reinforcement learning algorithms for inference routing in multi-user wireless InferNets.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.