Distributed or Centralized: Edge-Cloud Collaborative Active Learning

Active Learning (AL) aims to improve model performance by selecting the most informative samples from a pool of unlabeled data under a limited annotation budget. However, most existing active learning methods are designed for centralized settings and cannot be directly applied to edge-cloud collaborative learning systems, which have been widely adopted for edge intelligence. In this work, we propose Edge-Cloud Collaborative Active Learning (ECAL), an efficient two-stage data selection framework. ECAL employs distributed edge nodes to collaboratively pre-filter data using coordination information generated by the cloud, thereby reducing data transmission overhead between edge nodes and the cloud. Furthermore, ECAL offloads part of the data selection workload from the cloud to edge nodes. Through extensive experiments, we demonstrate that ECAL significantly improves model performance under fixed annotation budgets compared with state-of-the-art non-collaborative baselines.