3 citations · 5 across the 5 of their papers we have counts for
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cs.LG2025
Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models
Yuning Yang, Han Yu, Tianrun Gao +2
The deep integration of foundation models (FM) with federated learning (FL) enhances personalization and scalability for diverse downstream tasks, making it crucial in sensitive do…
cs.LG2025
Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections
Wei Zhuo, Zhaohuan Zhan, Han Yu
Federated Learning (FL) on graph-structured data typically faces non-IID challenges, particularly in scenarios where each client holds a distinct subgraph sampled from a global gra…
cs.LG2024★ 2 cited
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning
Yuning Yang, Han Yu, Chuan Sun +5
Federated Learning (FL) is a collaborative machine learning paradigm for training models on local sensitive data with privacy protection. Pre-trained transformer-based models have…