仅传输所需内容：在联邦多语言机器翻译中学习高效通信
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation
AI总结：
针对联邦多语言机器翻译中通信受限问题，提出基于元学习的自适应参数选择方法MetaSend，通过动态阈值过滤参数提升传输效率，在有限通信预算下于不同语言分布数据集上显著优于基线模型。
摘要：
联邦学习（FL）是一种很有前景的分布式机器学习范式，支持多个客户端协同训练全局模型。本文聚焦于一个实际的联邦多语言学习场景：拥有各自语言特定数据的客户端旨在协同构建高质量神经机器翻译（NMT）模型。然而，实际网络系统中的通信限制为FL各方之间交换大规模NMT引擎带来了挑战。我们提出一种基于元学习的自适应参数选择方法MetaSend，可在基于FL的多语言NMT训练期间提升客户端模型传输的通信效率。该方法基于不同FL轮次间客户端的张量偏差，学习动态阈值以在传输前过滤参数，同时不影响NMT模型质量。通过在两个具有不同语言分布的NMT数据集上进行实验，我们证明在通信预算有限的情况下，MetaSend在翻译质量上显著优于基线方法。
英文摘要：
Federated learning (FL) is a promising distributed machine learning paradigm that enables multiple clients to collaboratively train a global model. In this paper, we focus on a practical federated multilingual learning setup where clients with their own language-specific data aim 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. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
