Guest Editorial Split Learning in Consumer Electronics for Smart Cities: Theories, Tools, Applications and Challenges

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F24%3A43908762" target="_blank" >RIV/60076658:12310/24:43908762 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10799005" target="_blank" >https://ieeexplore.ieee.org/document/10799005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TCE.2024.3422617" target="_blank" >10.1109/TCE.2024.3422617</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Guest Editorial Split Learning in Consumer Electronics for Smart Cities: Theories, Tools, Applications and Challenges

Popis výsledku v původním jazyce

In the present fast-moving society, the Internet of Things (IoT) is transforming the way services are used in different industries. While it has many benefits, there are also considerable obstacles, especially in the areas of computing power, safety, and handling data. With the continuous evolution and importance of consumer electronics (CE) in smart cities, there is an increasing demand for sustainable and effective solutions to deal with challenges such as widespread sensing, advanced computing, prediction, monitoring, and data sharing. The artificial intelligence (AI) has emerged as a crucial component in the IoT environment, highlighting the need for energy-efficient CE in urban areas. The state of art methods are required to maximize resource usage and maintain high-quality services for smart systems in healthcare, transportation, AI-powered sensing (AIeS), and sustainable networks. The split learning is a technique for distributed deep learning, shows great potential as a solution for these CE applications. It can greatly reduce numerous obstacles linked with intelligent services in smart cities. The split learning enables the training of deep neural networks or split neural networks (SplitNN) using AIeS on various data sources. This method enables the secure and efficient processing of data without the requirement of directly sharing raw labeled data, which is crucial in industries like healthcare, finance, security, and surveillance where data privacy and security are vital. This guest editorial discusses and presents split learning methods in CE applications for smart cities. Using split learning, researchers and developers can develop creative solutions to address resource efficiency, data security, and service quality issues across different smart city sectors as presented further. As the IoT grows and changes, incorporating split learning into CE applications influences the platform for future smart cities.

Název v anglickém jazyce

Guest Editorial Split Learning in Consumer Electronics for Smart Cities: Theories, Tools, Applications and Challenges

Popis výsledku anglicky

In the present fast-moving society, the Internet of Things (IoT) is transforming the way services are used in different industries. While it has many benefits, there are also considerable obstacles, especially in the areas of computing power, safety, and handling data. With the continuous evolution and importance of consumer electronics (CE) in smart cities, there is an increasing demand for sustainable and effective solutions to deal with challenges such as widespread sensing, advanced computing, prediction, monitoring, and data sharing. The artificial intelligence (AI) has emerged as a crucial component in the IoT environment, highlighting the need for energy-efficient CE in urban areas. The state of art methods are required to maximize resource usage and maintain high-quality services for smart systems in healthcare, transportation, AI-powered sensing (AIeS), and sustainable networks. The split learning is a technique for distributed deep learning, shows great potential as a solution for these CE applications. It can greatly reduce numerous obstacles linked with intelligent services in smart cities. The split learning enables the training of deep neural networks or split neural networks (SplitNN) using AIeS on various data sources. This method enables the secure and efficient processing of data without the requirement of directly sharing raw labeled data, which is crucial in industries like healthcare, finance, security, and surveillance where data privacy and security are vital. This guest editorial discusses and presents split learning methods in CE applications for smart cities. Using split learning, researchers and developers can develop creative solutions to address resource efficiency, data security, and service quality issues across different smart city sectors as presented further. As the IoT grows and changes, incorporating split learning into CE applications influences the platform for future smart cities.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS

ISSN

0098-3063

e-ISSN

1558-4127

Svazek periodika

70

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

4

Strana od-do

5814-5817

Kód UT WoS článku

001378122400021

EID výsledku v databázi Scopus

2-s2.0-85212768771