Federated Learning for Edge Computing: A Survey
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250685" target="_blank" >RIV/61989100:27240/22:10250685 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2076-3417/12/18/9124" target="_blank" >https://www.mdpi.com/2076-3417/12/18/9124</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app12189124" target="_blank" >10.3390/app12189124</a>
Alternative languages
Result language
angličtina
Original language name
Federated Learning for Edge Computing: A Survey
Original language description
New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global model by learning from multiple decentralized edge clients. Although FL methods offer several advantages, including scalability and data privacy, they also introduce some risks and drawbacks in terms of computational complexity in the case of heterogeneous devices. Internet of Things (IoT) devices may have limited computing resources, poorer connection quality, or may use different operating systems. This paper provides an overview of the methods used in FL with a focus on edge devices with limited computational resources. This paper also presents FL frameworks that are currently popular and that provide communication between clients and servers. In this context, various topics are described, which include contributions and trends in the literature. This includes basic models and designs of system architecture, possibilities of application in practice, privacy and security, and resource management. Challenges related to the computational requirements of edge devices such as hardware heterogeneity, communication overload or limited resources of devices are discussed.
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
20205 - Automation and control systems
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Sciences
ISSN
2076-3417
e-ISSN
2076-3417
Volume of the periodical
12
Issue of the periodical within the volume
18
Country of publishing house
CH - SWITZERLAND
Number of pages
36
Pages from-to
nestrankovano
UT code for WoS article
000858044200001
EID of the result in the Scopus database
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