In federated learning (FL), classifiers (e.g., deep networks) are trained on
datasets from multiple centers without exchanging data across them, and thus
improves sample efficiency. In the classical setting of FL, the same labeling
criterion is usually employed across all centers being involved in training.
This constraint greatly limits the applicability of FL. For example, standards
used for disease diagnosis are more likely to be different across clinical
centers, which mismatches the classical FL setting. In this paper, we consider
an important yet under-explored setting of FL, namely FL with mixed-type labels
where different labeling criteria can be employed by various centers, leading
to inter-center label space differences and challenging existing FL methods
designed for the classical setting. To effectively and efficiently train models
with mixed-type labels, we propose a theory-guided and model-agnostic approach
that can make use of the underlying correspondence between those label spaces
and can be easily combined with various FL methods such as FedAvg. We present
convergence analysis based on over-parameterized ReLU networks. We show that
the proposed method can achieve linear convergence in label projection, and
demonstrate the impact of the parameters of our new setting on the convergence
rate. The proposed method is evaluated and the theoretical findings are
validated on benchmark and medical datasets.

本文提出了面向混合标签的联邦学习方法，旨在解决不同中心之间标签的空间不匹配问题，并结合不同联邦学习方法，通过过参数化 ReLU 网络实现线性收敛的标签投影，通过基准和医学数据集验证理论分析的有效性。