195 citations · 209 across the 17 of their papers we have counts for
4 papers · 1 filter
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration
Xinghao Wu, Xuefeng Liu, Jianwei Niu +2
Personalized federated learning (PFL) reduces the impact of non-independent and identically distributed (non-IID) data among clients by allowing each client to train a personalized…
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space
Guogang Zhu, Xuefeng Liu, Shaojie Tang +3
Personalized federated learning (PFL) is a popular framework that allows clients to have different models to address application scenarios where clients' data are in different doma…
3Deformer: A Common Framework for Image-Guided Mesh Deformation
Hao Su, Xuefeng Liu, Jianwei Niu +2
We propose 3Deformer, a general-purpose framework for interactive 3D shape editing. Given a source 3D mesh with semantic materials, and a user-specified semantic image, 3Deformer c…
Unlocking the Potential of Federated Learning for Deeper Models
Haolin Wang, Xuefeng Liu, Jianwei Niu +2
Federated learning (FL) is a new paradigm for distributed machine learning that allows a global model to be trained across multiple clients without compromising their privacy. Alth…