The relevant features for a machine learning task may be aggregated from data sources collected on different nodes in a network. This problem, which we call decentralized prediction, creates a number of interesting systems challenges in managing data routing, placing computation, and time-synchronization. This paper presents EdgeServe, a machine learning system that can serve decentralized predictions. EdgeServe relies on a low-latency message broker to route data through a network to nodes that can serve predictions. EdgeServe relies on a series of novel optimizations that can tradeoff computation, communication, and accuracy. We evaluate EdgeServe on three decentralized prediction tasks: (1) multi-camera object tracking, (2) network intrusion detection, and (3) human activity recognition.

翻译：机器学习任务的相关特征可能聚合自网络中不同节点收集的数据源。这一被称为"去中心化预测"的问题，在数据路由管理、计算放置与时序同步方面带来了若干有趣的系统挑战。本文提出EdgeServe——一种可提供去中心化预测服务的机器学习系统。该系统依托低延迟消息代理，通过网络将数据路由至具备预测能力的节点。EdgeServe采用一系列创新性优化方法，可在计算、通信与准确性之间进行权衡。我们在三个去中心化预测任务上评估了EdgeServe：（1）多摄像头目标跟踪，（2）网络入侵检测，以及（3）人体活动识别。