# Federated Learning-Based Analysis of Human Sentiments and Physical Activities in Natural Disasters
###### Abstract  
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. federated learning; active learning; machine learning; human visual sentiment analysis; human activity analysis +
Footnote †: journal: Medical Image Analysis  
labeling data from a large unlabeled data pool in an automated manner using ML models that are trained on a significantly reduced hand-labeled dataset. The existing FL literature suggests that each client node is exposed to predefined manually annotated (supervised learning) training image sets. However, in some circumstances, for instance during semi-supervised and unsupervised learning, each client node may possess a vast amount of unlabeled data that can be utilized to train local models, thereby enhancing the efficiency of the global aggregation model. In this article, we present an innovative paradigm for FL that is empowered by AL to interactively aggregate a global model, while considering privacy protection in a multi-stake context using unlabeled data accessible at each client node. To achieve this, we used and assessed a range of AL techniques with diverse sampling and disagreement approaches. Specifically, two distinct sampling and disagreement procedures were investigated in detail using two pool-based approaches. First, the labels of those instances for which the current prediction is maximally uncertain are sequentially queried through uncertainty sampling. This approach mitigates several downsides, including the fact that uncertainty sampling tends to be skewed towards the legitimate learner and might overlook key instances that are hidden from the estimator's perspective. Second, this issue is addressed using query by committee (QBC), which involves maintaining many hypotheses consecutively and selecting queries among them when discrepancies appear. In the proposed approach, we first keep the AL procedure offline to prevent complexity in terms of communication and learner biases at each node, with communications with the server initiated after the dataset at each node is labeled. At the onset, offline AL limits the number of communication cycles during FL and restricts the selection of data in every cycle. Furthermore, because the global aggregator model acts as a learner in this scenario to acquire and label training datasets during FL, for instance at every communication cycle, it potentially impedes the convergence of the global aggregator model owing to the data sampling bias. In this scenario, there are two primary obstacles. Initially, if the sample size to be selected for every cycle is kept extremely large, the global aggregator model efficiency will become more variable. Secondarily, maintaining a smaller sample size may expand the number of communication cycles.