  Federated learning is a decentralized approach for training models on
distributed devices, by summarizing local changes and sending aggregate
parameters from local models to the cloud rather than the data itself. In this
research we employ the idea of transfer learning to federated training for next
word prediction (NWP) and conduct a number of experiments demonstrating
enhancements to current baselines for which federated NWP models have been
successful. Specifically, we compare federated training baselines from randomly
initialized models to various combinations of pretraining approaches including
pretrained word embeddings and whole model pretraining followed by federated
fine tuning for NWP on a dataset of Stack Overflow posts. We realize lift in
performance using pretrained embeddings without exacerbating the number of
required training rounds or memory footprint. We also observe notable
differences using centrally pretrained networks, especially depending on the
datasets used. Our research offers effective, yet inexpensive, improvements to
federated NWP and paves the way for more rigorous experimentation of transfer
learning techniques for federated learning.
