{"type":"ScholarlyArticle","title":"Inconsistency-Based Federated Active Learning","authors":[{"name":"Chen-Chen Zong","slug":"chen-chen-zong","given":"Chen-Chen","family":"Zong","affiliation":""},{"name":"Tong Jin","slug":"tong-jin","given":"Tong","family":"Jin","affiliation":""},{"name":"Sheng-Jun Huang","slug":"sheng-jun-huang","given":"Sheng-Jun","family":"Huang","affiliation":""}],"abstract":"Federated learning (FL) enables distributed collaborative learning across local clients while preserving data privacy. However, its practical application in weakly supervised learning (WSL), where only a small subset of data is labeled, remains underexplored. Active learning (AL) is a promising solution for label-limited scenarios, but its adaptation to federated settings presents unique challenges, such as data heterogeneity and noise. In this paper, we propose Inconsistency-based Federated Active Learning (IFAL), a novel approach to address these challenges. First, we introduce a data-driven probability formulation that aligns the biases between local and global models in heterogeneous FL settings. Next, to mitigate noise, we propose an inter-model inconsistency criterion that filters out noisy examples and focuses on those with beneficial prediction discrepancies. Additionally, we introduce an intra-model inconsistency criterion to query examples that help refine the model’s decision boundaries. By combining these strategies with clustering, IFAL effectively selects a diverse and informative query set. Extensive experiments on benchmark datasets demonstrate that IFAL outperforms state-of-the-art methods.","doi":"10.24963/ijcai.2025/812","url":"https://ijcai.org/proceedings/2025/812","pdf":"https://ijcai.org/proceedings/2025/0812.pdf","markdown":"https://ijcai.org/proceedings/2025/812.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/812","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":812,"pages":{"first":7300,"last":7308},"keywords":["Machine Learning: ML: Active learning","Machine Learning: ML: Weakly supervised learning","Machine Learning: ML: Federated learning","Machine Learning: ML: Classification"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}