Convergence of update aware device scheduling for federated learning at the wireless edge

MM Amiri, D Gündüz, SR Kulkarni… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …
被引用次数：181 相关文章

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

MM Amiri, D Gunduz, SR Kulkarni… - IEEE Transactions on …, 2021 - oar.princeton.edu
We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

MM Amiri, D Gunduz, SR Kulkarni, HV Poor - arXiv preprint arXiv …, 2020 - arxiv.org
We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …

[引用][C] Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

MM Amiri, D Gunduz, SR Kulkarni… - IEEE Transactions on …, 2021 - cir.nii.ac.jp
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless
Edge | CiNii Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ移動 検索 …
[PDF] researchgate.net

[PDF][PDF] Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

MM Amiri, D Gündüz, SR Kulkarni… - arXiv preprint arXiv …, 2020 - researchgate.net
We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

MM Amiri, D Gunduz, SR Kulkarni… - IEEE Transactions on …, 2021 - collaborate.princeton.edu
We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

MM Amiri, D Gunduz, SR Kulkarni, HV Poor - arXiv e-prints, 2020 - ui.adsabs.harvard.edu
We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

MM Amiri, D Gunduz, SR Kulkarni… - IEEE TRANSACTIONS …, 2021 - iris.unimore.it
We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …
[PDF] imperial.ac.uk

[PDF][PDF] Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

MM Amiri, D Gündüz, SR Kulkarni, HV Poor - imperial.ac.uk
We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …
[PDF] researchgate.net

[PDF][PDF] Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

MM Amiri, D Gündüz, SR Kulkarni, HV Poor - researchgate.net
We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …