Unnecessary Budget Reduction in Federated Active Learning

Federated active learning has been proposed as a potential solution to limited labeling budget in federated learning. The global model contains balanced knowledge across clients, while the local model provides a better fit to local knowledge. As the number of locally labeled samples increases, the local model can be iterated locally, resulting in a richer local knowledge. Therefore, using the predictive consistency of the global model and the iterative local models to filter pseudo-labels can improve the correctness of pseudo-labels. Based on this, we propose unnecessary budget reduction(UBR). UBR provides the oracle with inconsistent candidate samples to request true labels, while consistent candidate samples are given pseudo-labels. In order to maintain the model performance, candidate samples with pseudo labels are added to the model training. A progressive training strategy is employed to avoid pseudo-labeling noise affecting model training, in which pseudo-labeled samples are added to model training only when the pseudo-labeling correctness rate is sufficiently high. We conduct experiments combining UBR with several federated active learning methods, which demonstrates UBR effectively reduces the unnecessary query budget while maintaining model performance.