Toward Efficient Transfer Learning in 6G

TL;DR

This paper explores how transfer learning can be effectively implemented in 6G networks to enhance data-driven applications, addressing challenges like dynamic data and high data collection costs.

Contribution

It introduces performance metrics for transfer learning success in 6G and demonstrates how infrastructure and data features can be adapted for efficient transfer learning.

Findings

Simulation shows weight transfer reduces overheads and improves performance.

Spatio-temporal data features can be exploited for better transfer learning.

Frameworks for integrating transfer learning into 6G infrastructure are proposed.

Abstract

6G networks will greatly expand the support for data-oriented, autonomous applications for over the top (OTT) and networking use cases. The success of these use cases will depend on the availability of big data sets which is not practical in many real scenarios due to the highly dynamic behavior of systems and the cost of data collection procedures. Transfer learning (TL) is a promising approach to deal with these challenges through the sharing of knowledge among diverse learning algorithms. with TL, the learning rate and learning accuracy can be considerably improved. However, there are implementation challenges to efficiently deploy and utilize TL in 6G. In this paper, we initiate this discussion by providing some performance metrics to measure the TL success. Then, we show how infrastructure, application, management, and training planes of 6G can be adapted to handle TL. We provide examples of TL in 6G and highlight the spatio-temporal features of data in 6G that can lead to efficient TL. By simulation results, we demonstrate how transferring the quantized neural network weights between two use cases can make a trade-off between overheads and performance and attain more efficient TL in 6G. We also provide a list of future research directions in TL for 6G.