  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
https://github.com/VityaVitalich/MeritFed
