  The increasing cloudification and softwarization of networks foster the
interplay among multiple independently managed deployments. An appealing reason
for such an interplay lies in distributed Machine Learning (ML), which allows
the creation of robust ML models by leveraging collective intelligence and
computational power. In this paper, we study the application of the two
cornerstones of distributed learning, namely Federated Learning (FL) and
Knowledge Distillation (KD), on the Wi-Fi Access Point (AP) load prediction use
case. The analysis conducted in this paper is done on a dataset that contains
real measurements from a large Wi-Fi campus network, which we use to train the
ML model under study based on different strategies. Performance evaluation
includes relevant aspects for the suitability of distributed learning operation
in real use cases, including the predictive performance, the associated
communication overheads, or the energy consumption. In particular, we prove
that distributed learning can improve the predictive accuracy centralized ML
solutions by up to 93% while reducing the communication overheads and the
energy cost by 80%.
