Dp-mtfl: Differentially Private Multi-Tier Federated Learning for IoT applications

Ramin Soleimani; Dirk Pesch

doi:10.1109/SMARTCOMP61445.2024.00042

Dp-mtfl: Differentially Private Multi-Tier Federated Learning for IoT applications

Ramin Soleimani
, Dirk Pesch

School of Computer Science and Information Technology

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Original language	English
Title of host publication	2024 Ieee International Conference On Smart Computing, Smartcomp 2024
Pages	158-165
Number of pages	8
DOIs	https://doi.org/10.1109/SMARTCOMP61445.2024.00042
Publication status	Published - 2024

Keywords

Differential Privacy
Federated Learning
Internet of Things
Sampled Gaussian Mechanism

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10.1109/SMARTCOMP61445.2024.00042

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@inproceedings{e8641f12a7c9441ca1f0859e0d5f7956,

title = "Dp-mtfl: Differentially Private Multi-Tier Federated Learning for IoT applications",

keywords = "Differential Privacy, Federated Learning, Internet of Things, Sampled Gaussian Mechanism",

author = "Ramin Soleimani and Dirk Pesch",

year = "2024",

doi = "10.1109/SMARTCOMP61445.2024.00042",

language = "English",

pages = "158--165",

booktitle = "2024 Ieee International Conference On Smart Computing, Smartcomp 2024",

}

TY - GEN

T1 - Dp-mtfl

T2 - Differentially Private Multi-Tier Federated Learning for IoT applications

AU - Soleimani, Ramin

AU - Pesch, Dirk

PY - 2024

Y1 - 2024

KW - Differential Privacy

KW - Federated Learning

KW - Internet of Things

KW - Sampled Gaussian Mechanism

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U2 - 10.1109/SMARTCOMP61445.2024.00042

DO - 10.1109/SMARTCOMP61445.2024.00042

M3 - Conference proceeding

SP - 158

EP - 165

BT - 2024 Ieee International Conference On Smart Computing, Smartcomp 2024

ER -

Dp-mtfl: Differentially Private Multi-Tier Federated Learning for IoT applications

Keywords

Access to Document

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