  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.
