Distributed and Quantized Kernel-based Learning over Interconnected Sensing Systems

基于核的学习被广泛用于非线性函数学习，这是各种机器学习问题中的一般任务，例如，分类与回归这导致其在许多具有挑战性的任务中的模式识别和数据分析中的广泛应用，包括但不限于各种互连传感系统（如传感器和物联网网络）中的时间序列预测。例如，传感器测量温度、湿度、压力或其他物理现象以预测未来的测量，相机拍摄照片或视频以识别物体。在许多应用中，多个接入节点在感兴趣的特定地理区域上收集和/或传播信息。此外，传感系统包括许多具有计算能力的设备，如智能手机、无人机、汽车等。在这种分布式网络中，数据和计算能力都是分布式的。不希望将收集的数据传输回中央实体进行处理。此外，不可能通过网络实时传输大量收集的数据。此外，在许多应用中，存在关于传输个人数据的有效安全和隐私问题，例如在医疗和金融应用中。因此，该提案旨在设计分布式和量化的学习算法，这些算法不会通过网络传输收集的数据。这项研究的结果将通过传统的学术场所广泛传播到整个研究界，政府和行业。建议的活动的结果将被整合到课程和教育研究计划在UCI，这是一个西班牙裔服务机构（HSI）。我们提出了在线分布式和量化的基于内核的学习算法，计算一些“本地”更新和通信相应的“更新”到他们的邻居，这样的网络可以学习一个“全球”的模型设计。这是在不通过具有静态和动态网络架构的感测系统传输所收集的数据的情况下完成的。我们建议正式提出不同的分布式和量化的函数学习算法，并研究其性能，包括其收敛性和遗憾分析。我们的算法将针对不同的网络结构设计，同时考虑网络延迟和动态。我们还提出了自适应分布式和量化算法的设计，并研究了它们的性能和遗憾分析。此外，我们研究了在这些情况下的最佳网络资源分配和相应的计算精度和网络资源之间的权衡。我们的目标是在分布式传感系统上设计鲁棒的分布式和量化的核学习算法，这些算法只需要与相邻节点进行通信，对网络特性（如网络拓扑和延迟）不太敏感。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。

Kernel-based learning is widely used for nonlinear function learning, which is a general task in various machine learning problems, e.g., classification and regression. This leads to its wide application in pattern recognition and data analysis in many challenging tasks, including but not limited to time series prediction in various interconnected sensing systems such as sensor and IoT networks. For example, sensors measure the temperature, humidity, pressure, or other physical phenomena to predict future measures, and cameras take pictures, or videos to recognize an object. In many applications, multiple access nodes collect and/or disseminate information over a certain geographical area of interest. In addition, sensing systems include many computationally capable devices like smartphones, UAVs, cars, and so on. In such distributed networks, both data and computational power are distributed. Transmitting the collected data back to a central entity for processing is not desired. Also, it is impossible to transmit the massive amount of collected data in real-time over networks. In addition, in many applications, there are valid security and privacy concerns about transmitting personal data, for example in medical and finance applications. Therefore, the proposal aims to design distributed and quantized learning algorithms that do not transmit the collected data over networks. The results of this research will be disseminated broadly through traditional scholarly venues to the entire research community, government, and industry. The results of the proposed activity will be integrated into coursework and educational research initiatives at UCI, which is a Hispanic-serving institution (HSI).We propose the design of online distributed and quantized kernel-based learning algorithms that calculate some "local" updates and communicate the corresponding "updates" to their neighbors such that collectively the network can learn a "global" model. This is done without transmitting the collected data over sensing systems with static and dynamic network architectures. We propose to formally present different distributed and quantized function learning algorithms and study their performance including their convergence and regret analysis. Our algorithms will be designed for different network structures while accounting for network delays and dynamics. We also propose the design of adaptive distributed and quantized algorithms and study their performance and regret analysis. In addition, we study the optimal network resource allocation in these scenarios and the corresponding trade-off between computation accuracy and network resources. Our goal is to design robust distributed and quantized kernel learning algorithms over distributed sensing systems that only need to communicate with neighboring nodes and are less sensitive to network characteristics, like network topology and delays.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.