Robust Searching-Based Gradient Collaborative Management in Intelligent Transportation System

With the rapid development of big data and the Internet of Things (IoT), traffic data from an Intelligent Transportation System (ITS) is becoming more and more accessible. To understand and simulate the traffic patterns from the traffic data, Multimedia Cognitive Computing (MCC) is an efficient and practical approach. Distributed Machine Learning (DML) has been the trend to provide sufficient computing resources and efficiency for MCC tasks to handle massive data and complex models. DML can speed up computation with those computing resources but introduces communication overhead. Gradient collaborative management or gradient aggregation in DML for MCC tasks is a critical task. An efficient managing algorithm of the communication schedules for gradient aggregation in ITS can improve the performance of MCC tasks. However, existing communication schedules typically rely on specific physical connection matrices, which have low robustness when a malfunction occurs. In this article, we propose Robust Searching-based Gradient Collaborative Management (RSGCM) in Intelligent Transportation System, a practical ring-based gradient managing algorithm for communication schedules across devices to deal with ITS malfunction. RSGCM provides solutions of communication schedules to various kinds of connection matrices with an acceptable amount of training time. Our experimental results have shown that RSGCM can deal with more varieties of connection matrices than existing state-of-the-art communication schedules. RSGCM also increases the robustness of ITS since it can restore the system's functionality in an acceptable time when device or connection breakdown happens.