8 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Text-to-Model: Text-Conditioned Neural Network Diffusion for Train-Once-for-All Personalization
Zexi Li, Lingzhi Gao, Chao Wu
Generative artificial intelligence (GenAI) has made significant progress in understanding world knowledge and generating content from human languages across various modalities, lik…
cs.LG2023★ 2 cited
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning
Lingzhi Gao, Zexi Li, Yang Lu +1
Personalized federated learning (pFL) enables collaborative training among multiple clients to enhance the capability of customized local models. In pFL, clients may have heterogen…
cs.LG2022★ 8 cited
FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks
Lingzhi Gao, Zhenyuan Zhang, Chao Wu
While existing federated learning approaches primarily focus on aggregating local models to construct a global model, in realistic settings, some clients may be reluctant to share…