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

Abstract

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).

OES-Fed: a federated learning framework in vehicular network based on noise data filtering

“…Since federated learning is an emerging field, its use in handling noise data is rarely covered. So this article refers to Ahmed et al (2020) , Ye et al (2020) , Li, Wang & Guan (2019) , Xu et al (2022) , Seth, Swain & Mishra (2018) , Zhang et al (2018) for a comparative analysis of federated learning algorithms applied to different domains with the mechanism proposed in this article, as shown in Table 1 .…”

An Optimized Multi-Task Learning Model for Disaster Classification and Victim Detection in Federated Learning Environments

“…The scheme in [8] also considered FL based single-task disaster classification, with additional concern regarding the annotation burden for each local training. Armed with AL, the authors reported that the proposed AL-based FL framework performs equally well under two strategies namely uncertainty sampling and query by committee.…”

“…A few recent works such as [8][9] have demonstrated the promising performance of disaster classification via FL. However, training-level evaluation results do not necessarily translate into good inference performance.…”

A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification

“…As a result, the classification model trained on an actively selected subset of annotated instances reaches in average a superior performance to a model trained on a randomly selected subset. AL strategies have been successfully employed in several applications, e.g., malware detection [21], waste classification [22], classification of medical images [23], and training of robots [24]. However, many of these AL strategies make three central assumptions that limit their practical use [25], and we refer to them as traditional AL.…”