Artificial Intelligence for Internet of Things as a Service: Small or Big Data, Private or Public Model, Centralized or Federated Learning?

被引：0

作者：

Lin, Ying-Dar ^{[1
]}

Lai, Yuan-Cheng ^{[2
]}

Sudyana, Didik ^{[3
]}

Hwang, Ren-Hung ^{[4
]}

机构：

[1] Natl Yang Ming Chiao Tung Univ, Comp Sci, Hsinchu 300, Taiwan

[2] Natl Taiwan Univ Sci & Technol, Dept Informat Management, Taipei 106, Taiwan

[3] Natl Yang Ming Chiao Tung Univ, Elect Engn & Comp Sci Int Grad Program, Hsinchu 300, Taiwan

[4] Natl Yang Ming Chiao Tung Univ, Coll Artificial Intelligence, Hsinchu 300, Taiwan

来源：

COMPUTER | 2023年 / 56卷 / 12期

关键词：

Training; Cloud computing; Federated learning; Computational modeling; Big Data; Data models; Internet of Things; CLOUD; EDGE;

D O I：

10.1109/MC.2023.3303370

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a generic framework for mapping the training and federation tasks of three artificial intelligence for Internet of Things as a service (AIoTaS) systems to multiple cloud-edge-fog paradigms. A total of 31 possible mappings are identified as possible reference designs for AIoTaS providers.

引用

页码：65 / 79

页数：15

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