Anomaly detection in double-entry bookkeeping data by federated learning system with non-model sharing approach.
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| Title: | Anomaly detection in double-entry bookkeeping data by federated learning system with non-model sharing approach. |
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| Authors: | Mashiko S; Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan., Kawamata Y; Center for Artificial Intelligence Research, Tsukuba Institute for Advanced Research, University of Tsukuba, Tsukuba, Japan. yjkawamata@gmail.com., Nakayama T; Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan., Sakurai T; Center for Artificial Intelligence Research, Tsukuba Institute for Advanced Research, University of Tsukuba, Tsukuba, Japan.; Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan., Okada Y; Center for Artificial Intelligence Research, Tsukuba Institute for Advanced Research, University of Tsukuba, Tsukuba, Japan.; Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan. |
| Source: | Scientific reports [Sci Rep] 2025 Nov 26; Vol. 15 (1), pp. 42208. Date of Electronic Publication: 2025 Nov 26. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2045-2322 |
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| DOI: | 10.1038/s41598-025-26120-y |
