We present Cross-Client Label Propagation(XCLP), a new method for transductive federated learning. XCLP estimates a data graph jointly from the data of multiple clients and computes labels for the unlabeled data by propagating label information across the graph. To avoid clients having to share their data with anyone, XCLP employs two cryptographically secure protocols: secure Hamming distance computation and secure summation. We demonstrate two distinct applications of XCLP within federated learning. In the first, we use it in a one-shot way to predict labels for unseen test points. In the second, we use it to repeatedly pseudo-label unlabeled training data in a federated semi-supervised setting. Experiments on both real federated and standard benchmark datasets show that in both applications XCLP achieves higher classification accuracy than alternative approaches.

翻译：我们提出跨客户端标签传播（XCLP），一种用于直推式联邦学习的新方法。XCLP从多个客户端的数据中联合估计数据图，并通过跨图传播标签信息来计算未标记数据的标签。为了避免客户端与任何他人共享数据，XCLP采用两种密码学安全协议：安全汉明距离计算和安全求和。我们展示了XCLP在联邦学习中的两种不同应用。第一种应用中，我们以一次性方式使用它来预测未见测试点的标签；第二种应用中，我们在联邦半监督场景中反复使用它为未标记训练数据生成伪标签。在真实联邦和标准基准数据集上的实验表明，在这两种应用中，XCLP比替代方法实现了更高的分类准确率。