Ameliorating Federated Learning using Dynamic Inertia Weight-Based Advanced Particle Swarm Optimization for Consumer Electronic Devices
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10259125" target="_blank" >RIV/61989100:27240/25:10259125 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11130483" target="_blank" >https://ieeexplore.ieee.org/document/11130483</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TCE.2025.3600307" target="_blank" >10.1109/TCE.2025.3600307</a>
Alternative languages
Result language
angličtina
Original language name
Ameliorating Federated Learning using Dynamic Inertia Weight-Based Advanced Particle Swarm Optimization for Consumer Electronic Devices
Original language description
The extensive utilization of consumer electronic devices such as smartphones, smart wearables, and smart home technology has resulted in significant surge in data production. Due to storage and data transfer limitations, traditional machine learning techniques are sometimes impracticable for these distributed systems and can cause serious privacy problems. Federated Learning mitigates these issues by maintaining data on local devices. It updates models by consolidating locally trained outcomes on central server, which can be crucial in case of consumer electronic devices. Thus, to tackle these issues, this article presents new method named Dynamic inertia weight-based federated advanced particle swarm optimization (DIW-FedAPSO). It uses dynamic inertia weight strategy in advanced particle swarm optimization to select inertia weight dynamically for providing optimal velocity to consumer electronic devices and transmitting obtained optimal score after performing the local training instead of sending and averaging weights of devices as traditional federated learning method does. The experimental evaluations on different datasets (CelebA, FFHQ) under different non-iid data heterogeneity settings shows that proposed method attains better accuracy, while maintaining data privacy and enhances communication efficiency while minimizing number of communication rounds required to attain targeted accuracy over all datasets than other currently existing methods. © 2025 Elsevier B.V., All rights reserved.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Consumer Electronics
ISSN
0098-3063
e-ISSN
1558-4127
Volume of the periodical
71
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
12345-12357
UT code for WoS article
001643440300019
EID of the result in the Scopus database
2-s2.0-105013774365