3 papers
cs.LG2024
Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
Jun Luo, Chen Chen, Shandong Wu
Federated prompt learning benefits federated learning with CLIP-like Vision-Language Model's (VLM's) robust representation learning ability through prompt learning. However, curren…
cs.CV2023
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning
Guangyu Sun, Matias Mendieta, Jun Luo +2
Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments. Partial model personalization has been proposed…
cs.LG2022
PGFed: Personalize Each Client's Global Objective for Federated Learning
Jun Luo, Matias Mendieta, Chen Chen +1
Personalized federated learning has received an upsurge of attention due to the mediocre performance of conventional federated learning (FL) over heterogeneous data. Unlike convent…