  Federated learning (FL) is a promising approach to distributed compute, as
well as distributed data, and provides a level of privacy and compliance to
legal frameworks. This makes FL attractive for both consumer and healthcare
applications. While the area is actively being explored, few studies have
examined FL in the context of larger language models and there is a lack of
comprehensive reviews of robustness across tasks, architectures, numbers of
clients, and other relevant factors. In this paper, we explore the fine-tuning
of Transformer-based language models in a federated learning setting. We
evaluate three popular BERT-variants of different sizes (BERT, ALBERT, and
DistilBERT) on a number of text classification tasks such as sentiment analysis
and author identification. We perform an extensive sweep over the number of
clients, ranging up to 32, to evaluate the impact of distributed compute on
task performance in the federated averaging setting. While our findings suggest
that the large sizes of the evaluated models are not generally prohibitive to
federated training, we found that the different models handle federated
averaging to a varying degree. Most notably, DistilBERT converges significantly
slower with larger numbers of clients, and under some circumstances, even
collapses to chance level performance. Investigating this issue presents an
interesting perspective for future research.
