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
收藏
页码：65 / 79
页数：15
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