Yuru Liu — accepted AI-conference papers

2026

Fair-FedMOE: Group-Fair One-Shot Federated Learning via Prototype-Guided Experts for Medical Imaging Analysis

Lingzhao Meng, Shuai Guo, Weishan Zhang, Zengxiang Li, Han Yu, Nan Liu +4

ICML 2026poster

Group fairness can ensure equitable performance across different demographic subgroups for medical image analysis. However, the current fine-tuned foundation models (FMs) exhibit significant subgroup disparity. One-shot federated learning (OFL) can potentially mitigate this by leveraging cross-insti…

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2026

FedUP: Uncertainty-Aware Personalized Federated Learning via Probabilistic Prototypes

Furui Qi, Weishan Zhang, Lingzhao Meng, Yuru Liu, Zijun Feng, Yuange Liu +3

IJCAI 2026

Prototype-based federated learning enables efficient knowledge sharing by exchanging class prototypes rather than full model parameters. However, heterogeneous client data and limited local samples increase prototype estimation variance, making many client prototypes unreliable. Existing methods usu

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