  Most studies in cross-device federated learning focus on small models, due to
the server-client communication and on-device computation bottlenecks. In this
work, we leverage various techniques for mitigating these bottlenecks to train
larger language models in cross-device federated learning. With systematic
applications of partial model training, quantization, efficient transfer
learning, and communication-efficient optimizers, we are able to train a $21$M
parameter Transformer and $20.2$M parameter Conformer that achieve the same or
better perplexity as that of a similarly sized LSTM with $\sim10\times$ smaller
client-to-server communication cost and $11\%$ lower perplexity than smaller
LSTMs commonly studied in literature.
