Device sampling for heterogeneous federated learning: Theory, algorithms, and implementation

S Wang, M Lee, S Hosseinalipour… - … -IEEE Conference on …, 2021 - ieeexplore.ieee.org
The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …
被引用次数：109 相关文章

Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation

S Wang, M Lee, S Hosseinalipour, R Morabito… - arXiv e …, 2021 - ui.adsabs.harvard.edu
The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …

[引用][C] Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation

S Wang, M Lee, S Hosseinalipour, R Morabito… - IEEE INFOCOM 2021 …, 2021 - cir.nii.ac.jp
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and
Implementation | CiNii Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ …
[PDF] researchgate.net

[PDF][PDF] Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation

S Wang, M Lee, S Hosseinalipour, R Morabito… - researchgate.net
The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …

Device sampling for heterogeneous federated learning: Theory, algorithms, and implementation

S Wang, M Lee, S Hosseinalipour… - 40th IEEE …, 2021 - collaborate.princeton.edu
The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …

[引用][C] Device sampling for heterogeneous federated learning: Theory, algorithms, and implementation

S Wang, M Lee, S Hosseinalipour… - 40th IEEE …, 2021 - researchportal.helsinki.fi
Device sampling for heterogeneous federated learning: Theory, algorithms, and implementation
— University of Helsinki Skip to main navigation Skip to search Skip to main content University …

Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation

S Wang, M Lee, S Hosseinalipour, R Morabito… - arXiv preprint arXiv …, 2021 - arxiv.org
The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …

Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation

S Wang, M Lee, S Hosseinalipour, R Morabito… - IEEE INFOCOM 2021 …, 2021 - dl.acm.org
The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …