In recent years, Artificial Intelligence (AI) has become an increasingly central actor in our lives, enabling technological solutions to adapt to the immediate needs of the end-users. The shift from a static to a continuously evolving technology does not come for free but presents a critical cost in terms of communication bandwidth, computational power, and other network resources. Hence, telecommunication networks are facing competition between traditional data flows and those supporting the training of AI algorithms. This leads to a new dilemma: how can we balance network resources among end users and learning agents?

The scientific community has started being aware of the issues associated with the communication and computational overhead due to AI. Despite this, most works still assume that learning and user data are exchanged on separate channels, ignoring the dependency between the agent training and network conditions. To overcome such a limit, it is necessary to investigate how to implement AI solutions from a new perspective.

This project aims to define new strategies for combining the optimization of network resources and learning algorithms, analyzing how the improvement of the AI training implies a resource reduction for target applications. The project consists of identifying significant use cases where the "cost of learning" dilemma arises, modeling the scenarios theoretically, and proposing new solutions for the described problem.

For more information, contact Federico Mason at federico.mason@unipd.it