Federated Multilingual Neural Machine Translation (Fed-MNMT) has emerged as a
promising paradigm for institutions with limited language resources. This
approach allows multiple institutions to act as clients and train a unified
model through model synchronization, rather than collecting sensitive data for
centralized training. This significantly reduces the cost of corpus collection
and preserves data privacy. However, as pre-trained language models (PLMs)
continue to increase in size, the communication cost for transmitting
parameters during synchronization has become a training speed bottleneck. In
this paper, we propose a communication-efficient Fed-MNMT framework that
addresses this issue by keeping PLMs frozen and only transferring lightweight
adapter modules between clients. Since different language pairs exhibit
substantial discrepancies in data distributions, adapter parameters of clients
may conflict with each other. To tackle this, we explore various clustering
strategies to group parameters for integration and mitigate the negative
effects of conflicting parameters. Experimental results demonstrate that our
framework reduces communication cost by over 98% while achieving similar or
even better performance compared to competitive baselines. Further analysis
reveals that clustering strategies effectively solve the problem of linguistic
discrepancy and pruning adapter modules further improves communication
efficiency.

本文提出了一种新的联邦式多语言神经机器翻译框架。相比中心化训练，该框架通过模型同步，只传输轻量化的适配器模块，降低了模型同步的通信代价。作者同时探索了各种聚类策略来减少冲突，并在适配器模块上进行了修剪，成功地提出了通信效率。实验结果表明，该框架在实现相当甚至更好的性能的同时，可以将通信代价降低 98% 以上。