CVPR 2023 - Re-thinking Federated Active Learning based on Inter-class Diversity

In this episode we discuss Re-thinking Federated Active Learning based on Inter-class Diversity by SangMook Kim, Sangmin Bae, Hwanjun Song, Se-Young Yun. The paper discusses the use of federated active learning (FAL) frameworks in situations where a significant amount of unlabeled data is present. The authors demonstrate that the effectiveness of available query selector models depends on the global and local inter-class diversity. They propose LoGo, a FAL sampling strategy that integrates both "global" and "local-only" models and consistently outperforms six other active learning strategies in various experimental settings. The code for LoGo is available on GitHub.