FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
Xinyuan Ji Zhaowei Zhu Wei Xi Olga Gadyatskaya Zilong Song Yong Cai Yang Liu
Federated Learning (FL) heavily depends on label quality for its performance.
However, the label distribution among individual clients is always both noisy
and heterogeneous. The high loss incurred by client-specific samples in
heterogeneous label noise poses challenges for distinguishing between
client-specific and noisy label samples, impacting the effectiveness of
existing label noise learning approaches. To tackle this issue, we propose
FedFixer, where the personalized model is introduced to cooperate with the
global model to effectively select clean client-specific samples. In the dual
models, updating the personalized model solely at a local level can lead to
overfitting on noisy data due to limited samples, consequently affecting both
the local and global models' performance. To mitigate overfitting, we address
this concern from two perspectives. Firstly, we employ a confidence regularizer
to alleviate the impact of unconfident predictions caused by label noise.
Secondly, a distance regularizer is implemented to constrain the disparity
between the personalized and global models. We validate the effectiveness of
FedFixer through extensive experiments on benchmark datasets. The results
demonstrate that FedFixer can perform well in filtering noisy label samples on
different clients, especially in highly heterogeneous label noise scenarios.