  Recently, federated learning (FL) has achieved wide successes for diverse
privacy-sensitive applications without sacrificing the sensitive private
information of clients. However, the data quality of client datasets can not be
guaranteed since corresponding annotations of different clients often contain
complex label noise of varying degrees, which inevitably causes the performance
degradation. Intuitively, the performance degradation is dominated by clients
with higher noise rates since their trained models contain more misinformation
from data, thus it is necessary to devise an effective optimization scheme to
mitigate the negative impacts of these noisy clients. In this work, we propose
a two-stage framework FedELC to tackle this complicated label noise issue. The
first stage aims to guide the detection of noisy clients with higher label
noise, while the second stage aims to correct the labels of noisy clients' data
via an end-to-end label correction framework which is achieved by learning
possible ground-truth labels of noisy clients' datasets via back propagation.
We implement sixteen related methods and evaluate five datasets with three
types of complicated label noise scenarios for a comprehensive comparison.
Extensive experimental results demonstrate our proposed framework achieves
superior performance than its counterparts for different scenarios.
Additionally, we effectively improve the data quality of detected noisy
clients' local datasets with our label correction framework. The code is
available at https://github.com/Sprinter1999/FedELC.
