Zhaobin Sun — accepted AI-conference papers

2026

FedRNC: Addressing Spatio-Temporal Label Misalignment in Federated Noisy Class-Incremental Learning

Xingwei Huang, Zhaobin Sun, Junjie Shi, Xin Yang, Zengqiang Yan

AAAI 2026technical

Federated class-incremental learning (FCIL) aims to incrementally learn new classes across decentralized clients under non-IID data distributions. However, the pervasive challenge of label noise in FCIL has been completely overlooked. In this work, we introduce federated noisy class-incremental lear

Cited by 0 Source ↗PDF ↗Scholar ↗

2024

FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise

Nannan Wu, Zhaobin Sun, Zengqiang Yan, Li Yu

AAAI 2024technical

Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property. However, existing research overlooks the prevalent annotation noise encountered in real-world medical datasets, which limits the perfo…

Cited by 9 Source ↗PDF ↗code ↗Scholar ↗