Efficient split-mix federated learning for on-demand and in-situ customization

J Hong, H Wang, Z Wang, J Zhou - arXiv preprint arXiv:2203.09747, 2022 - arxiv.org
Federated learning (FL) provides a distributed learning framework for multiple participants to
collaborate learning without sharing raw data. In many practical FL scenarios, participants …
被引用次数：46 相关文章
[PDF] nsf.gov

[PDF][PDF] Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization

J Hong, H Wang, Z Wang, J Zhou - International Conference on Learning …, 2022 - par.nsf.gov
Federated learning (FL) provides a distributed learning framework for multiple participants to
collaborate learning without sharing raw data. In many practical FL scenarios, participants …

Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization

J Hong - iclr.cc
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization Page 1 Efficient
Split-Mix Federated Learning for On-Demand and In-Situ Customization Junyuan Hong1 …

Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization

J Hong, H Wang, Z Wang, J Zhou - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
Federated learning (FL) provides a distributed learning framework for multiple participants to
collaborate learning without sharing raw data. In many practical FL scenarios, participants …

Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization

J Hong, H Wang, Z Wang, J Zhou - International Conference on Learning … - openreview.net
Federated learning (FL) provides a distributed learning framework for multiple participants to
collaborate learning without sharing raw data. In many practical FL scenarios, participants …