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:我们展示了跨精密 Label Propagation (XCLP) (XCLP), 这是一种传输联合学习的新方法。 XCLP 通过在图中传播标签信息,从多个客户的数据中联合估算一个数据图表,并对未贴标签的数据进行计算。 为避免客户不得不与任何人共享数据, XCLP 采用了两个加密安全协议: 安全模拟远程计算和安全比较。 我们展示了 XCLP 在联邦学习中的两个不同的应用。 首先, 我们用一个镜头来预测看不见测试点的标签。 第二, 我们用它来在联合半监督设置中反复使用伪标签不贴标签的培训数据。 对实际粘贴和标准基准数据集的实验显示, XCLP 在两个应用中都实现了比替代方法更高的分类精度。