  Training mixed-domain translation models is a complex task that demands
tailored architectures and costly data preparation techniques. In this work, we
leverage federated learning (FL) in order to tackle the problem. Our
investigation demonstrates that with slight modifications in the training
process, neural machine translation (NMT) engines can be easily adapted when an
FL-based aggregation is applied to fuse different domains. Experimental results
also show that engines built via FL are able to perform on par with
state-of-the-art baselines that rely on centralized training techniques. We
evaluate our hypothesis in the presence of five datasets with different sizes,
from different domains, to translate from German into English and discuss how
FL and NMT can mutually benefit from each other. In addition to providing
benchmarking results on the union of FL and NMT, we also propose a novel
technique to dynamically control the communication bandwidth by selecting
impactful parameters during FL updates. This is a significant achievement
considering the large size of NMT engines that need to be exchanged between FL
parties.
