Completely heterogeneous federated learning

C Liu, Y Yang, X Cai, Y Ding, H Lu - arXiv preprint arXiv:2210.15865, 2022 - arxiv.org
Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models,
and non-iid labels scenarios. Existing FL methods fail to handle the above three constraints …
被引用次数：10 相关文章
[PDF] researchgate.net

[PDF][PDF] COMPLETELY HETEROGENEOUS FEDERATED LEARNING

C Liu, Y Yang, X Cai, Y Ding, H Lu - researchgate.net
Federated learning (FL) faces three major difficulties: crossdomain, heterogeneous models,
and non-iid labels scenarios. Existing FL methods fail to handle the above three constraints …

Completely Heterogeneous Federated Learning

C Liu, Y Yang, X Cai, Y Ding, H Lu - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models,
and non-iid labels scenarios. Existing FL methods fail to handle the above three constraints …