3 papers
cs.CV2024
Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models
Matias Mendieta, Guangyu Sun, Chen Chen
Federated learning (FL) enables multiple clients to train models collectively while preserving data privacy. However, FL faces challenges in terms of communication cost and data he…
cs.CV2024
Towards Multi-modal Transformers in Federated Learning
Guangyu Sun, Matias Mendieta, Aritra Dutta +2
Multi-modal transformers mark significant progress in different domains, but siloed high-quality data hinders their further improvement. To remedy this, federated learning (FL) has…
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…