  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.
