FedCorr: Multi-Stage Federated Learning for Label Noise Correction

Understand

Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model.

• In real-world FL implementations, client data could have label noise, and different clients could have vastly different label noise levels.
• Although there exist methods in centralized learning for tackling label noise, such methods do not perform well on heterogeneous label noise in FL settings, due to the typically smaller sizes of client datasets and data privacy requirements in FL.
• In this paper, we propose $\texttt{FedCorr}$, a general multi-stage framework to tackle heterogeneous label noise in FL, without making any assumptions on the noise models of local clients, while still maintaining client data privacy.