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.

本文利用联邦学习来处理复杂的混合域翻译模型训练任务，证明了在训练过程中进行轻微的修改并采用基于联邦学习的聚合方法，可以轻松地适应不同域，从而构建出与依赖于集中式训练技术的最先进基准相媲美的神经机器翻译引擎。同时，此研究还在五个数据集上进行了验证，并论述了 FL 和 NMT 如何互相受益。此外，研究者们还提出了一种新的技术，通过在 FL 更新期间选择具有影响力的参数来动态控制通信带宽大小，针对需要在 FL 双方之间交换非常大的 NMT 引擎的情况，这是一项重要的成果。