5 papers
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…
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…
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…
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…