Revisiting Early-Learning Regularization When Federated Learning Meets
  Noisy Labels
Taehyeon Kim Donggyu Kim Se-Young Yun
In the evolving landscape of federated learning (FL), addressing label noise
presents unique challenges due to the decentralized and diverse nature of data
collection across clients. Traditional centralized learning approaches to
mitigate label noise are constrained in FL by privacy concerns and the
heterogeneity of client data. This paper revisits early-learning
regularization, introducing an innovative strategy, Federated Label-mixture
Regularization (FLR). FLR adeptly adapts to FL's complexities by generating new
pseudo labels, blending local and global model predictions. This method not
only enhances the accuracy of the global model in both i.i.d. and non-i.i.d.
settings but also effectively counters the memorization of noisy labels.
Demonstrating compatibility with existing label noise and FL techniques, FLR
paves the way for improved generalization in FL environments fraught with label
inaccuracies.