Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification

The feasibility of Federated Learning (FL) is highly dependent on the training and inference capabilities of local models, which are subject to the availability of meaningful and annotated data. The availability of such data is in turn contingent on the tedious and time-consuming annotation job that typically requires the manual analysis of training samples. Active Learning (AL) provides an alternative solution allowing a Machine Learning (ML) model to automatically choose and label the data from which it learns without involving manual inspection of each training sample. In this work, we explore how FL can benefit from unlabelled data available at each participating client using AL. To this aim, we propose an AL-based FL framework by employing and evaluating several AL methods in two different application domains. Through an extensive experimentation setup, we show that AL is equally useful in federated and centralized learning by achieving comparable results with manually labeled data using fewer samples without involving human annotators in collecting training data. We also demonstrated that the proposed method is dataset/application independent by evaluating the proposed method in two interesting applications, namely natural disaster analysis and waste classification, having different properties and challenges. Promising results are obtained on both applications resulting in comparable results against the best-case scenario where each sample is manually analyzed and annotated (Baseline 1), and improvement of 3.1% and 4% with best methods respectively over the training sets with irrelevant images on natural disaster and waste classification datasets (Baseline 2).