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Federating Dynamic Models using Early-Exit Architectures for Automatic
  Speech Recognition on Heterogeneous Clients

Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clients

27 May 2024
Mohamed Nabih Ali
A. Brutti
Daniele Falavigna
ArXivPDFHTML

Papers citing "Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clients"

4 / 4 papers shown
Title
When the Curious Abandon Honesty: Federated Learning Is Not Private
When the Curious Abandon Honesty: Federated Learning Is Not Private
Franziska Boenisch
Adam Dziedzic
R. Schuster
Ali Shahin Shamsabadi
Ilia Shumailov
Nicolas Papernot
FedML
AAML
64
180
0
06 Dec 2021
PhiNets: a scalable backbone for low-power AI at the edge
PhiNets: a scalable backbone for low-power AI at the edge
Francesco Paissan
Alberto Ancilotto
Elisabetta Farella
26
31
0
01 Oct 2021
Towards Personalized Federated Learning
Towards Personalized Federated Learning
A. Tan
Han Yu
Li-zhen Cui
Qiang Yang
FedML
AI4CE
180
832
0
01 Mar 2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets
  with Ordered Dropout
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Samuel Horváth
Stefanos Laskaridis
Mario Almeida
Ilias Leondiadis
Stylianos I. Venieris
Nicholas D. Lane
176
267
0
26 Feb 2021
1