Data-free knowledge distillation for heterogeneous federated learning

Z Zhu, J Hong, J Zhou - International conference on machine …, 2021 - proceedings.mlr.press
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …
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[PDF] mlr.press

[PDF][PDF] Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Z Zhu, J Hong, J Zhou - proceedings.mlr.press
Federated Learning (FL) is a decentralized machine-learning paradigm in which a global
server iteratively aggregates the model parameters of local users without accessing their …
[PDF] nsf.gov

[PDF][PDF] Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Z Zhu, J Hong, J Zhou - … of the 38th International Conference on Machine …, 2021 - par.nsf.gov
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively aggregates the model parameters of local users without accessing their …

Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Z Zhu, J Hong, J Zhou - Proceedings of machine learning …, 2021 - pubmed.ncbi.nlm.nih.gov
Federated Learning (FL) is a decentralized machine-learning paradigm in which a global
server iteratively aggregates the model parameters of local users without accessing their …

Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Z Zhu, J Hong, J Zhou - arXiv preprint arXiv:2105.10056, 2021 - arxiv.org
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …
[HTML] nih.gov

[HTML][HTML] Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Z Zhu, J Hong, J Zhou - Proceedings of machine learning research, 2021 - ncbi.nlm.nih.gov
Federated Learning (FL) is a decentralized machine-learning paradigm in which a global
server iteratively aggregates the model parameters of local users without accessing their …

Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Z Zhu, J Hong, J Zhou - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …

Data-Free Knowledge Distillation for Heterogeneous Federated Learning.

Z Zhu, J Hong, J Zhou - Proceedings of Machine Learning Research, 2021 - europepmc.org
Federated Learning (FL) is a decentralized machine-learning paradigm in which a global
server iteratively aggregates the model parameters of local users without accessing their …