Computer Science > Machine Learning
arXiv:2210.06434 (cs)
[Submitted on 12 Oct 2022 (v1), last revised 12 Dec 2023 (this version, v4)]
Title:Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning
View a PDF of the paper titled Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning, by Jonathan Scott and 2 other authors
View PDFHTML (experimental)Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2210.06434 [cs.LG] |
| (orarXiv:2210.06434v4 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2210.06434 arXiv-issued DOI via DataCite |
Submission history
From: Jonathan Scott [view email][v1] Wed, 12 Oct 2022 17:33:47 UTC (85 KB)
[v2] Wed, 1 Feb 2023 12:30:54 UTC (117 KB)
[v3] Wed, 28 Jun 2023 07:47:48 UTC (75 KB)
[v4] Tue, 12 Dec 2023 09:05:34 UTC (134 KB)
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View a PDF of the paper titled Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning, by Jonathan Scott and 2 other authors
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