Abstract

The first method for implementing this mobile AI paradigm is to treat each device in the network as a rational and autonomous decision-maker, which acquires its own local dataset and uses it to create its own local ANN model. In terms of data sharing and processing, this method eliminates the need for network infrastructure and edge users to communicate, and it has the ability to make the 5G vision of distributed, self-managing networks a reality. Although mobile devices may not be able to construct accurate models on their own, the ensuing difference in performance must be examined because of the restricted storage and processing capabilities they have. As a result, it unclear whether or not a stable network operating point can be reached, let alone whether or not it efficient. One method to answer the final two questions is to use the Noble-prize-winner framework of game theory, which gives sophisticated numerical tools for analysing the interactions among independent decision makers in games of chance. Game theory has already been extensively employed in wireless communication networks, but it has never been applied in conjunction with deep learning.