  Federated Learning (FL) is a distributed machine learning paradigm that
enables learning models from decentralized private datasets, where the labeling
effort is entrusted to the clients. While most existing FL approaches assume
high-quality labels are readily available on users' devices; in reality, label
noise can naturally occur in FL and is closely related to clients'
characteristics. Due to scarcity of available data and significant label noise
variations among clients in FL, existing state-of-the-art centralized
approaches exhibit unsatisfactory performance, while prior FL studies rely on
excessive on-device computational schemes or additional clean data available on
server. Here, we propose FedLN, a framework to deal with label noise across
different FL training stages; namely, FL initialization, on-device model
training, and server model aggregation, able to accommodate the diverse
computational capabilities of devices in a FL system. Specifically, FedLN
computes per-client noise-level estimation in a single federated round and
improves the models' performance by either correcting or mitigating the effect
of noisy samples. Our evaluation on various publicly available vision and audio
datasets demonstrate a 22% improvement on average compared to other existing
methods for a label noise level of 60%. We further validate the efficiency of
FedLN in human-annotated real-world noisy datasets and report a 4.8% increase
on average in models' recognition performance, highlighting that~\method~can be
useful for improving FL services provided to everyday users.
