RINGS: Walk For Resiliency & Privacy: A Random Walk Framework for Learning at the Edge

下一代（NextG）无线系统的学习将带来一场技术和社会革命，甚至比数据给早期以语音为中心的系统带来的革命还要大。为了支持物联网（IoT）、联合学习、移动医疗、自动驾驶汽车等应用，必须对主要源自边缘和用户设备的数据进行学习。越来越多的研究工作集中在学习过程中使用边缘，这在更好地利用网络资源、减少延迟、对云不可用性和灾难性故障的弹性以及提高安全性和隐私性方面是有利的。然而，目前提出的解决方案主要存在一个关键的集中式组件（通常在云中），该组件负责组织和聚合节点的计算。这种严格的集中式基础设施可能会抑制NextG系统中弹性和隐私的全部潜力。通过放松集中式基础设施，提出的研究旨在推进随机行走学习算法，作为分布式学习和网络联合设计的统一框架的基础，以弹性和隐私为首要目标。在随机漫步学习中，模型可以被认为是一个“接力棒”，它被更新并从网络中的一个节点（云，边缘节点，终端设备等）传递到它的一个智能选择的邻居。然后，这个接力棒可以按照规定的时间表和/或自适应地作为随机游走的一部分传递给云，从而允许一个流体架构，其中集中化和完全去中心化构成两个角点。拟议的工作将侧重于随机漫步学习在NextG中的适用性的主要挑战和机遇，即：(i)对数据的异质性和网络的异质性和动态性的适应性；（ii）通过编码理论冗余方法面对故障时的弹性和优雅退化；（iii）节点间模型分布和随机行走蛇；及（iv）本地拥有资料的私隐。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。

Learning in Next Generation (NextG) wireless systems is expected to bring about a technological and societal revolution even bigger than that which data brought to early voice-centered systems. Learning will have to be performed on data predominantly originating at edge and user devices in order to support applications such as Internet of Things (IoT), federated learning, mobile healthcare, self-driving cars, and others. A growing body of research work has focused on engaging the edge in the learning process, which can be advantageous in terms of a better utilization of network resources, delay reduction, resiliency against cloud unavailability and catastrophic failures, and increased security and privacy. Present proposed solutions, however, predominantly suffer from having a critical centralized component, typically in the cloud, that organizes and aggregates the nodes’ computations. This rigid centralized infrastructure can inhibit the full potential of resiliency and privacy in NextG systems. By relaxing the centralized infrastructure, the proposed research aims to advance Random Walk learning algorithms as the basis of a unified framework for the joint design of distributed learning and networking, with resiliency and privacy being the overarching goal.In Random Walk learning, the model can be thought of as a “baton” that is updated and passed from one node (cloud, edge node, end-devices, etc.) in the network to one of its neighbors that is smartly chosen. This baton can be then passed to the cloud at a prescribed schedule and/or adaptively as part of the random walk, allowing thus a fluid architecture where centralization and full decentralization constitute two corner points. The proposed work will focus on major challenges and opportunities specific to the applicability of random walk learning in NextG, namely: (i) Adaptability to the heterogeneity of the data and the heterogeneity and dynamic nature of the network; (ii) Resiliency and graceful degradation in the face of failures via coding-theoretic redundancy methods; (iii) Model distribution across nodes and random walking snakes; and (iv) Privacy of the locally owned data.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.