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（Cross-Client Label Propagation, 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，并通过在图上传播标签信息来计算未带标签数据的标签。为避免客户端必须与任何人共享其数据，XCLP采用两种加密安全协议：安全汉明距离计算与安全求和。我们展示了XCLP在联邦学习中的两种不同应用。在第一种应用中，我们将其以一次性方式用于预测未见测试点的标签。在第二种应用中，我们将其重复用于在联邦半监督设置中对未带标签的训练数据进行伪标签标注。在真实联邦数据集和标准基准数据集上的实验表明，在这两种应用中，XCLP均实现了比替代方法更高的分类准确率。