On the Adoption of Distributed Intelligence Toward 6G Networks

Abstract

Distributed intelligence (DI) is becoming increasingly important in mobile networks due to privacy and regulatory restrictions on data movement. This trend is fueled by an increase in data volume for training ML models, advancements in device computing/storage capabilities, and edge network nodes near data collection points. Techniques such as Federated and Split Learning, applied in horizontal and vertical feature spaces, enable efficient training of machine learning models while maintaining data privacy. Despite the adoption of DI techniques in mobile networks, there are still gaps and areas in mobile network architecture where DI is either not applied or not taking advantage of its full potential. To that end, this paper provides a state-of-the-art overview of the adoption of DI in mobile networks from the perspective of standardization. We identify the importance of machine learning model lifecycle management (ML LCM) and position DI as an extension of that. From this overview, gaps and limitations are identified and presented. To overcome these limitations, we perform a thought experiment which extends ML LCM with DI capability from Core Network (CN) to Radio Access Networks (RAN) which are distributed by nature. In that setting a set of Key Performance Indicators (KPIs) to quantify the performance of different DI techniques are introduced. These KPIs aim at quantifying computational overhead, memory/storage requirements, communication footprint, model efficiency and robustness, flexibility and privacy. We evaluate these KPIs on a RAN use case, that of secondary carrier prediction and show the interplay between model efficiency and computational footprint which are important metrics to consider when migrating from a centralized to a distributed setting. Finally, we provide additional enhancements to improve these KPIs and thus further promote the adoption of DI techniques.