| Summary: | In federated learning (FL), in addition to the training and speculating capacities of the global and local models, an appropriately annotated dataset is equally crucial. These datasets rely on annotation procedures that are error prone and laborious, which require personal inspection for training the overall dataset. In this study, we evaluate the effect of unlabeled data supplied by every participating node in active learning (AL) on the FL. We propose an AL-empowered FL paradigm that combines two application scenarios and assesses different AL techniques. We demonstrate the efficacy of AL by attaining equivalent performance in both centralized and FL with well-annotated data, utilizing limited data images with reduced human assistance during the annotation of the training sets. We establish that the proposed method is independent of the datasets and applications by assessing it using two distinct datasets and applications, human sentiments and human physical activities during natural disasters. We achieved viable results on both application domains that were relatively comparable to the optimal case, in which every data image was manually annotated and assessed (criterion 1). Consequently, a significant improvement of 5.5–6.7% was achieved using the active learning approaches on the training sets of the two datasets, which contained irrelevant images.
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