We consider learning a multi-class classification model in the federated
setting, where each user has access to the positive data associated with only a
single class. As a result, during each federated learning round, the users need
to locally update the classifier without having access to the features and the
model parameters for the negative classes. Thus, naively employing conventional
decentralized learning such as the distributed SGD or Federated Averaging may
lead to trivial or extremely poor classifiers. In particular, for the embedding
based classifiers, all the class embeddings might collapse to a single point.
To address this problem, we propose a generic framework for training with
only positive labels, namely Federated Averaging with Spreadout (FedAwS), where
the server imposes a geometric regularizer after each round to encourage
classes to be spreadout in the embedding space. We show, both theoretically and
empirically, that FedAwS can almost match the performance of conventional
learning where users have access to negative labels. We further extend the
proposed method to the settings with large output spaces.

本文提出了一个通用的培训框架 FedaWS，它保证了类别在嵌入空间中分散，从而可以学习具有仅正标签的多类分类模型。这个框架可以几乎达到具有负标签访问权限的传统学习的性能。