Federated Active Learning (F-AL): An Efficient Annotation Strategy for Federated Learning
This project proposes to apply active learning (AL) to the FL framework to reduce the annotation workload, and empirically demonstrates that the F-AL outperforms baseline methods in image classification tasks.
Abstract
Federated learning (FL) has been intensively investigated in terms of communication efficiency, privacy, and fairness. However, efficient annotation, which is a pain point in real-world FL applications, is less studied. In this project, we propose to apply active learning (AL) to the FL framework to reduce the annotation workload. We expect that the AL and FL can improve the performance of each other complementarily. In our proposed federated active learning (F-AL) method, the clients collaboratively execute the AL to obtain the instances which are considered informative to FL in a distributed optimization manner. We compare the test accuracies of the global FL models using the conventional random sampling strategy, client-level separate AL (S-AL), and the proposed F-AL. We empirically demonstrate that the F-AL outperforms baseline methods in image classification tasks.