We present a new approach based on the Personalized Federated Learning
algorithm MeritFed that can be applied to Natural Language Tasks with
heterogeneous data. We evaluate it on the Low-Resource Machine Translation
task, using the dataset from the Large-Scale Multilingual Machine Translation
Shared Task (Small Track #2) and the subset of Sami languages from the
multilingual benchmark for Finno-Ugric languages. In addition to its
effectiveness, MeritFed is also highly interpretable, as it can be applied to
track the impact of each language used for training. Our analysis reveals that
target dataset size affects weight distribution across auxiliary languages,
that unrelated languages do not interfere with the training, and auxiliary
optimizer parameters have minimal impact. Our approach is easy to apply with a
few lines of code, and we provide scripts for reproducing the experiments at
this https URL

基于个性化联邦学习算法 MeritFed 的新方法，适用于具有异构数据的自然语言任务，在低资源机器翻译任务中，我们使用来自大规模多语言机器翻译共享任务（小轨道 2）的数据集和芬乌格里亚语族多语言基准测试中子集的萨米语进行评估。除了其有效性外，MeritFed 也具有高解释性，因为它可以应用于跟踪每种训练语言的影响。我们的分析结果显示目标数据集大小影响辅助语言之间的权重分布，无关语言不会干扰训练，辅助优化器参数的影响很小。我们的方法易于应用，只需几行代码，并提供了用于重现实验的脚本。