Federated learning (FL) is a promising approach for solving multilingual
tasks, potentially enabling clients with their own language-specific data to
collaboratively construct a high-quality neural machine translation (NMT)
model. However, communication constraints in practical network systems present
challenges for exchanging large-scale NMT engines between FL parties. In this
paper, we propose a meta-learning-based adaptive parameter selection
methodology, MetaSend, that improves the communication efficiency of model
transmissions from clients during FL-based multilingual NMT training. Our
approach learns a dynamic threshold for filtering parameters prior to
transmission without compromising the NMT model quality, based on the tensor
deviations of clients between different FL rounds. Through experiments on two
NMT datasets with different language distributions, we demonstrate that
MetaSend obtains substantial improvements over baselines in translation quality
in the presence of a limited communication budget.

本文提出了一种基于元学习的自适应参数选择方法 MetaSend，通过学习客户端在不同联邦学习轮次之间的张量偏差，提高了联邦学习多语言神经机器翻译中模型传输过程的通信效率，并在有限通信预算下，在两个具有不同语言分布的数据集上的翻译质量方面表现出了显著提升。