  Robustness is becoming another important challenge of federated learning in
that the data collection process in each client is naturally accompanied by
noisy labels. However, it is far more complex and challenging owing to varying
levels of data heterogeneity and noise over clients, which exacerbates the
client-to-client performance discrepancy. In this work, we propose a robust
federated learning method called FedRN, which exploits k-reliable neighbors
with high data expertise or similarity. Our method helps mitigate the gap
between low- and high-performance clients by training only with a selected set
of clean examples, identified by their ensembled mixture models. We demonstrate
the superiority of FedRN via extensive evaluations on three real-world or
synthetic benchmark datasets. Compared with existing robust training methods,
the results show that FedRN significantly improves the test accuracy in the
presence of noisy labels.
