  Federated learning (FL) collaboratively trains a shared global model
depending on multiple local clients, while keeping the training data
decentralized in order to preserve data privacy. However, standard FL methods
ignore the noisy client issue, which may harm the overall performance of the
shared model. We first investigate critical issue caused by noisy clients in FL
and quantify the negative impact of the noisy clients in terms of the
representations learned by different layers. We have the following two key
observations: (1) the noisy clients can severely impact the convergence and
performance of the global model in FL, and (2) the noisy clients can induce
greater bias in the deeper layers than the former layers of the global model.
Based on the above observations, we propose Fed-NCL, a framework that conducts
robust federated learning with noisy clients. Specifically, Fed-NCL first
identifies the noisy clients through well estimating the data quality and model
divergence. Then robust layer-wise aggregation is proposed to adaptively
aggregate the local models of each client to deal with the data heterogeneity
caused by the noisy clients. We further perform the label correction on the
noisy clients to improve the generalization of the global model. Experimental
results on various datasets demonstrate that our algorithm boosts the
performances of different state-of-the-art systems with noisy clients. Our code
is available on https://github.com/TKH666/Fed-NCL
