| Autor: |
Hanyue Xu, Kah Phooi Seng, Li Minn Ang, Jeremy Smith |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 101016-101052 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3422211 |
| Popis: |
The advent of the Artificial Intelligent Internet of Things (AIoT) has sparked a revolution in the deployment of intelligent systems, driving the need for innovative data processing techniques. Due to escalating data privacy concerns and the immense volume of data produced by IoT devices, decentralized and distributed learning methods that are rapidly replacing traditional centralized learning play a pivotal role. As AIoT systems become increasingly ubiquitous, the accompanying computational and storage demands necessitate a departure from conventional paradigms towards more scalable, distributed, and decentralized architectures. This paper delves into the background of AIoT, with a particular focus on the evolution of distributed and decentralized learning mechanisms that operate without the need for centralized data collection, thus aligning with the General Data Protection Regulation (GDPR) for enhanced data privacy. The various distributed and decentralized learning strategies are the focus of this paper that facilitate collaborative model training across multiple AIoT nodes, thereby not only improving the performance of the AIoT system but also mitigating the risks of data concentration. The review further explores the adaptability of AI algorithms in these distributed settings, assessing their potential to optimize system performance and learning efficacy. The paper concludes with some use cases and lessons learned for decentralized and distributed learning in various AIoT areas. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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