Fedclip: Fast generalization and personalization for clip in federated learning

W Lu, X Hu, J Wang, X Xie - arXiv preprint arXiv:2302.13485, 2023 - arxiv.org
Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …
被引用次数：34 相关文章

FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning

W Lu, X Hu, J Wang, X Xie - 2023 - sites.computer.org
Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning

W Lu, X Hu, J Wang, X Xie - arXiv e-prints, 2023 - ui.adsabs.harvard.edu
Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

FEDCLIP: FAST GENERALIZATION AND PERSONALIZATION FOR CLIP IN FEDERATED LEARNING

W Lu, HU Xixu, J Wang, X Xie - ICLR 2023 Workshop on Trustworthy and … - openreview.net
When federated learning (FL) meets trustworthy and reliable large-scale models, two critical
challenges come: data distribution heterogeneity and high resource costs. Specifically, the …