Existing federated classification algorithms typically assume the local
annotations at every client cover the same set of classes. In this paper, we
aim to lift such an assumption and focus on a more general yet practical
non-IID setting where every client can work on non-identical and even disjoint
sets of classes (i.e., client-exclusive classes), and the clients have a common
goal which is to build a global classification model to identify the union of
these classes. Such heterogeneity in client class sets poses a new challenge:
how to ensure different clients are operating in the same latent space so as to
avoid the drift after aggregation? We observe that the classes can be described
in natural languages (i.e., class names) and these names are typically safe to
share with all parties. Thus, we formulate the classification problem as a
matching process between data representations and class representations and
break the classification model into a data encoder and a label encoder. We
leverage the natural-language class names as the common ground to anchor the
class representations in the label encoder. In each iteration, the label
encoder updates the class representations and regulates the data
representations through matching. We further use the updated class
representations at each round to annotate data samples for locally-unaware
classes according to similarity and distill knowledge to local models.
Extensive experiments on four real-world datasets show that the proposed method
can outperform various classical and state-of-the-art federated learning
methods designed for learning with non-IID data.

本文提出了一种基于自然语言类名（class names）在非同分布（non-IID）数据上进行联邦学习（federated learning）的方法，将分类问题转化为数据表示和类名表示之间的匹配，并将分类模型分为数据编码器和标签编码器进行处理，通过更新类别表示来提高该算法在客户端独占类（client-exclusive classes）场景下的分类准确性。