Fast-convergent federated learning with adaptive weighting
Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …
global model under the orchestration of a central server while keeping privacy-sensitive data …
Fast-Convergent Federated Learning with Adaptive Weighting
H Wu, P Wang - arXiv e-prints, 2020 - ui.adsabs.harvard.edu
Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …
global model under the orchestration of a central server while keeping privacy-sensitive data …
[PDF][PDF] Fast-Convergent Federated Learning With Adaptive Weighting
H Wu, P Wang - researchgate.net
Federated learning(FL) enables resourceconstrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …
global model under the orchestration of a central server while keeping privacy-sensitive data …
[PDF][PDF] Fast-Convergent Federated Learning With Adaptive Weighting
H Wu, P Wang - hongdawu1226.github.io
Federated learning(FL) enables resourceconstrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …
global model under the orchestration of a central server while keeping privacy-sensitive data …
Fast-Convergent Federated Learning with Adaptive Weighting
H Wu, P Wang - ICC 2021-IEEE International Conference on …, 2021 - ieeexplore.ieee.org
Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …
global model under the orchestration of a central server while keeping privacy-sensitive data …
Fast-Convergent Federated Learning with Adaptive Weighting
H Wu, P Wang - arXiv preprint arXiv:2012.00661, 2020 - arxiv.org
Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …
global model under the orchestration of a central server while keeping privacy-sensitive data …
[PDF][PDF] Fast-Convergent Federated Learning with Adaptive Weighting
H Wu, P Wang - researchgate.net
Federated learning(FL) enables resourceconstrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …
global model under the orchestration of a central server while keeping privacy-sensitive data …
[PDF][PDF] Fast-Convergent Federated Learning with Adaptive Weighting
H Wu, P Wang - hongdawu1226.github.io
Federated learning(FL) enables resourceconstrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …
global model under the orchestration of a central server while keeping privacy-sensitive data …