A Study of Virtual Concept Drift in Federated Data Stream Learning

With the widespread application of FL across various domains, learning from data streams and addressing concept drift (i.e. distributional changes in data) have emerged as a crucial research focus in this field. As a major type of concept drift, virtual drift, however, has received very little attention so far. This study aims to provide a deep understanding of how virtual drift in streaming data can affect FL models and how it can be detected and overcome effectively. We propose a FL framework FL-HVD, to tackle virtual drifting data. Based on this framework, firstly, we characterise virtual drift with four spatial features and design 11 virtual drifting scenarios to investigate its impact. Secondly, we study and compare six distribution-based and unsupervised drift detection techniques to identify virtual drift. We find that the distribution-based methods outperform the unsupervised ones in terms of accuracy and timeliness in general, among which Ks_2samp is the best. Thirdly, we explore the effectiveness of three adaptation methods to minimize the negative impact of virtual drift once it is detected. The experimental results demonstrate significant improvements and performance stability achieved by applying the FL-HVD framework combined with the Ks_2samp detector and having more local training rounds in training FL models with virtual drifting data. This paper provides valuable insights and guidance for addressing virtual drift in FL.