  Federated learning (FL) aims at training a global model on the server side
while the training data are collected and located at the local devices. Hence,
the labels in practice are usually annotated by clients of varying expertise or
criteria and thus contain different amounts of noises. Local training on noisy
labels can easily result in overfitting to noisy labels, which is devastating
to the global model through aggregation. Although recent robust FL methods take
malicious clients into account, they have not addressed local noisy labels on
each device and the impact to the global model. In this paper, we develop a
simple two-level sampling method "FedNoiL" that (1) selects clients for more
robust global aggregation on the server; and (2) selects clean labels and
correct pseudo-labels at the client end for more robust local training. The
sampling probabilities are built upon clean label detection by the global
model. Moreover, we investigate different schedules changing the local epochs
between aggregations over the course of FL, which notably improves the
communication and computation efficiency in noisy label setting. In experiments
with homogeneous/heterogeneous data distributions and noise ratios, we observed
that direct combinations of SOTA FL methods with SOTA noisy-label learning
methods can easily fail but our method consistently achieves better and robust
performance.
