FedNets: Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks

Besher Alhalabi; Shadi Basurra; Mohamed Gaber

doi:10.1109/ACCESS.2023.3261266

FedNets: Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks

Besher Alhalabi (Corresponding / Lead Author), Shadi Basurra (Corresponding / Lead Author), Mohamed Gaber (Corresponding / Lead Author)

Research output: Contribution to journal › Article › peer-review

8 Citations (SciVal)

Original language	English
Pages (from-to)	30726-30738
Number of pages	13
Journal	IEEE Access
Volume	11
DOIs	https://doi.org/10.1109/ACCESS.2023.3261266
Publication status	Published (VoR) - 24 Mar 2023

Keywords

Federated learning
affinity propagation
convolutional neural networks
ensemble learning
graph embedding
non-IID datasets
privacy

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10.1109/ACCESS.2023.3261266

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title = "FedNets: Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks",

keywords = "Federated learning, affinity propagation, convolutional neural networks, ensemble learning, graph embedding, non-IID datasets, privacy",

author = "Besher Alhalabi and Shadi Basurra and Mohamed Gaber",

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TY - JOUR

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T2 - Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks

AU - Alhalabi, Besher

AU - Basurra, Shadi

AU - Gaber, Mohamed

N1 - Publisher Copyright: Author

PY - 2023/3/24

Y1 - 2023/3/24

KW - Federated learning

KW - affinity propagation

KW - convolutional neural networks

KW - ensemble learning

KW - graph embedding

KW - non-IID datasets

KW - privacy

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U2 - 10.1109/ACCESS.2023.3261266

DO - 10.1109/ACCESS.2023.3261266

M3 - Article

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VL - 11

SP - 30726

EP - 30738

JO - IEEE Access

JF - IEEE Access

ER -

FedNets: Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks

Keywords

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