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https://ruomo.lib.uom.gr/handle/7000/1714
Πλήρης εγγραφή μεταδεδομένων
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Stergiou, Konstantinos D. | - |
dc.contributor.author | Psannis, Kostas E. | - |
dc.date.accessioned | 2023-11-09T10:37:55Z | - |
dc.date.available | 2023-11-09T10:37:55Z | - |
dc.date.issued | 2022-12 | - |
dc.identifier | 10.1109/TNSM.2022.3197059 | en_US |
dc.identifier.issn | 1932-4537 | en_US |
dc.identifier.issn | 2373-7379 | en_US |
dc.identifier.uri | https://doi.org/10.1109/TNSM.2022.3197059 | en_US |
dc.identifier.uri | https://ruomo.lib.uom.gr/handle/7000/1714 | - |
dc.description.abstract | Traffic sign recognition and autonomous vehicles computing are a few of the innovative applications which are emerging in the domain of mobile edge computing. Distributed machine learning in the form of Federated Learning (FL) has been applied to mobile edge computing through a range of methodologies and techniques for intelligent feature classification approaches. The challenges that research on such FL methods is facing is twofold: identify an optimal distributed architecture and algorithm components to each side to meet the demand of heavy data processing, and enhance the algorithm components with heuristics that fit to the problem domain and optimize the key parameters of the algorithms. In this prospect, we present a Federated Learning implementation based on a neural network architecture with emphasis to traffic sign image recognition. Our benchmark was tested with two FL strategies seeking an optimal performance model and in reference to a corresponding data set. We present the results of this work while we define the scope of future improvements to our model. | en_US |
dc.language.iso | en | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source | IEEE Transactions on Network and Service Management | en_US |
dc.subject | FRASCATI::Engineering and technology | en_US |
dc.subject | FRASCATI::Natural sciences | en_US |
dc.subject.other | Federated learning | en_US |
dc.subject.other | mobile edge computing | en_US |
dc.subject.other | traffic sign image recognition | en_US |
dc.subject.other | convolutional neural networks | en_US |
dc.title | Federated Learning Approach Decouples Clients From Training a Local Model and With the Communication With the Server | en_US |
dc.type | Article | en_US |
dc.contributor.department | Τμήμα Εφαρμοσμένης Πληροφορικής | en_US |
local.identifier.volume | 19 | en_US |
local.identifier.issue | 4 | en_US |
local.identifier.firstpage | 4213 | en_US |
local.identifier.lastpage | 4218 | en_US |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
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TNSM-2022-05053_final.pdf | 359,79 kB | Adobe PDF | Προβολή/Ανοιγμα |
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