Kaiqi Guan — accepted AI-conference papers

2025

DKDR: Dynamic Knowledge Distillation for Reliability in Federated Learning

Yueyang Yuan, Wenke Huang, Guancheng Wan, Kaiqi Guan, He Li, Mang Ye

NeurIPS 2025poster

Federated Learning (FL) has demonstrated a promising future in privacy-friendly collaboration but it faces the data heterogeneity problem. Knowledge Distillation (KD) can serve as an effective method to address this issue. However, challenges arise from the unreliability of existing distillation met…

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2025

Rethinking Fair Federated Learning from Parameter and Client View

Kaiqi Guan, Wenke Huang, Xianda Guo, Yueyang Yuan, Bin Yang, Mang Ye

NeurIPS 2025poster

Federated Learning is a promising technique that enables collaborative machine learning while preserving participant privacy. With respect to multi-party collaboration, achieving performance fairness acts as a critical challenge in federated systems. Existing explorations mainly focus on considering…

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