Federated learning is a paradigm that enables local devices to jointly train
a server model while keeping the data decentralized and private. In federated
learning, since local data are collected by clients, it is hardly guaranteed
that the data are correctly annotated. Although a lot of studies have been
conducted to train the networks robust to these noisy data in a centralized
setting, these algorithms still suffer from noisy labels in federated learning.
Compared to the centralized setting, clients' data can have different noise
distributions due to variations in their labeling systems or background
knowledge of users. As a result, local models form inconsistent decision
boundaries and their weights severely diverge from each other, which are
serious problems in federated learning. To solve these problems, we introduce a
novel federated learning scheme that the server cooperates with local models to
maintain consistent decision boundaries by interchanging class-wise centroids.
These centroids are central features of local data on each device, which are
aligned by the server every communication round. Updating local models with the
aligned centroids helps to form consistent decision boundaries among local
models, although the noise distributions in clients' data are different from
each other. To improve local model performance, we introduce a novel approach
to select confident samples that are used for updating the model with given
labels. Furthermore, we propose a global-guided pseudo-labeling method to
update labels of unconfident samples by exploiting the global model. Our
experimental results on the noisy CIFAR-10 dataset and the Clothing1M dataset
show that our approach is noticeably effective in federated learning with noisy
labels.

提出了一种新的联邦学习方案，其中服务器与本地模型合作，通过交换类别的中心来保持一致的决策边界，实现了在网络中使用带有给定噪声的标签的一致决策边界。同时，还提出了选择有自信的样本来更新模型以及全局引导的伪标签方法来更新不自信的样本标签。在嘈杂的 CIFAR-10 数据集和 Clothing1M 数据集上的实验证明，该方法在使用有噪声的标签的联邦学习中非常有效。