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Cellular IoT Device Growth Expected to Skyrocket, Revenue Per Device to Decline

Cellular IoT Device Growth Expected to Skyrocket, Revenue Per Device to Decline
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Juniper Research , Low Power Wide Area , Cap Deployment Analytics , Profile Management , Client Selection , Federated Learning Servers ,

"A Comprehensive Overview of IoT-Based Federated Learning: Focusing on " by Naghmeh Khajehali, Jun Yan et al.

The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing how services and applications impact our daily lives. In traditional ML methods, data are collected and processed centrally. However, modern IoT networks face challenges in implementing this approach due to their vast amount of data and privacy concerns. To overcome these issues, federated learning (FL) has emerged as a solution. FL allows ML methods to achieve collaborative training by transferring model parameters instead of client data. One of the significant challenges of federated learning is that IoT devices as clients usually have different computation and communication capacities in a dynamic environment. At the same time, their network availability is unstable, and their data quality varies. To achieve high-quality federated learning and handle these challenges, designing the proper client selection process and methods are essential, which involves selecting suitable clients from the ....

Client Selection , Device Selection , Federated Learning , Machine Learning , Mode Selection , Participant Selection ,