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

Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning