3 citations · 5 across the 5 of their papers we have counts for
6 papers
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…
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…
Revisiting Topological Interference Management: A Learning-to-Code on Graphs Perspective
Zhiwei Shan, Xinping Yi, Han Yu +2
The advance of topological interference management (TIM) has been one of the driving forces of recent developments in network information theory. However, state-of-the-art coding s…
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data
Lynda Aouar, Han Yu
Adaptive causal representation learning from observational data is presented, integrated with an efficient sample splitting technique within the semiparametric estimating equation…
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…
Federated Neuro-Symbolic Learning
Pengwei Xing, Songtao Lu, Han Yu
Neuro-symbolic learning (NSL) models complex symbolic rule patterns into latent variable distributions by neural networks, which reduces rule search space and generates unseen rule…