41 citations · 75 across the 13 of their papers we have counts for
5 papers · 1 filter
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-Resolution
Yan Wang, Tongtong Su, Yusen Li +3
Recent research on deep convolutional neural networks (CNNs) has provided a significant performance boost on efficient super-resolution (SR) tasks by trading off the performance an…
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
Liping Yi, Han Yu, Zhuan Shi +4
Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model…
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing
Liping Yi, Han Yu, Gang Wang +1
As a privacy-preserving collaborative machine learning paradigm, federated learning (FL) has attracted significant interest from academia and the industry alike. To allow each data…
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
Liping Yi, Han Yu, Gang Wang +2
Federated learning (FL) is an emerging machine learning paradigm in which a central server coordinates multiple participants (clients) collaboratively to train on decentralized dat…
FedGH: Heterogeneous Federated Learning with Generalized Global Header
Liping Yi, Gang Wang, Xiaoguang Liu +2
Federated learning (FL) is an emerging machine learning paradigm that allows multiple parties to train a shared model collaboratively in a privacy-preserving manner. Existing horiz…