Theme and Scope The recent advancements in smart devices, mobile networks, and computing technologies are ushering us into a new era of intelligent IoT. To enable these smart devices to provide intelligent services, machine learning techniques are essential for training powerful predictive models. A common approach involves collecting distributed user data to a central cloud for deep learning model training. However, transferring massive amounts of data to the cloud center can cause significant transmission pressure on the backbone network. Additionally, increasing concerns about data privacy and the enforcement of privacy protection laws make it impractical to transmit data from end devices to the cloud. Despite these advancements, existing AI techniques face several limitations that require novel solutions to better comprehend and improve their potential for decision-making in various real-world applications. Key challenges for employing diverse AI techniques include network management, communication efficiency, client selection and scheduling, resource management, security and privacy concerns, incentive mechanisms, and service management and pricing. Addressing these challenges calls for various techniques, including but not limited to:
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ISSN 1607-9264
e-ISSN 2079-4029
JCR 2023 Impact Factor: 0.9
CiteScore 2023: 3.2
SJR 2023: 0.382
SNIP 2023: 0.483
- Open Access
- Peer Review
- Reasonable APC
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