{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:29:43Z","timestamp":1775744983041,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100014717","name":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2007"],"award-info":[{"award-number":["U21B2007"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Sci. Eng."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In recent years, the task of knowledge graph completion has attracted significant attention from researchers. In practical scenarios, multi-source knowledge graph completion is quite common. Federated knowledge graph embedding enables joint learning across multiple knowledge graphs while ensuring data privacy and security. Generally, each data source has a different data distribution. They may exhibit various  connections, such as combinatorial, hierarchical, and symmetric\/asymmetric connections. Existing federated knowledge graph models overlook the data heterogeneity of knowledge graphs from different sources, using a unified scoring function to assess the quality of the generated embedding vectors from different clients. This limitation affects the quality of knowledge graph embeddings generated by each client. Therefore, this paper proposes a federated knowledge graph embedding framework based on the\u00a0diffusion model. On the client side, we employ diffusion model to learn knowledge graph embeddings. We utilize the diffusion model's forward noise-adding process to learn the knowledge graph's distribution. We then use the reverse denoising process to generate knowledge embeddings directly. Additionally, we employ knowledge distillation during client model training to address the drift between local optimization and global convergence. Since the original data cannot leave the local environment in federated learning, we adopt a framework that shares diffusion models for federated knowledge graph completion. Extensive experiments demonstrate that our model significantly outperforms existing state-of-the-art methods in three benchmark datasets.<\/jats:p>","DOI":"10.1007\/s41019-025-00292-z","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T03:55:39Z","timestamp":1749527739000},"page":"639-652","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DFedKG: Diffusion-Based Federated Knowledge Graph Completion"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8288-6452","authenticated-orcid":false,"given":"Chao","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8696-9685","authenticated-orcid":false,"given":"Yurong","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0080-8857","authenticated-orcid":false,"given":"Boyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2091-5490","authenticated-orcid":false,"given":"Yi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"292_CR1","doi-asserted-by":"crossref","unstructured":"Zhang F, Yuan NJ, Lian D, Xie X, Ma W-Y (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 353\u2013362","DOI":"10.1145\/2939672.2939673"},{"key":"292_CR2","doi-asserted-by":"crossref","unstructured":"Hao Y, Zhang Y, Liu K, He S, Liu Z, Wu H, Zhao J (2017) An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 221\u2013231","DOI":"10.18653\/v1\/P17-1021"},{"key":"292_CR3","doi-asserted-by":"crossref","unstructured":"Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247\u20131250","DOI":"10.1145\/1376616.1376746"},{"key":"292_CR4","first-page":"1306","volume":"24","author":"A Carlson","year":"2010","unstructured":"Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka E, Mitchell T (2010) Toward an architecture for never-ending language learning. Proceed AAAI Conf Artific Intell 24:1306\u20131313","journal-title":"Proceed AAAI Conf Artific Intell"},{"issue":"10","key":"292_CR5","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2629489","volume":"57","author":"D Vrande\u010di\u0107","year":"2014","unstructured":"Vrande\u010di\u0107 D, Kr\u00f6tzsch M (2014) Wikidata: a free collaborative knowledgebase. Commun ACM 57(10):78\u201385","journal-title":"Commun ACM"},{"key":"292_CR6","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"292_CR7","doi-asserted-by":"publisher","first-page":"192435","DOI":"10.1109\/ACCESS.2020.3030076","volume":"8","author":"Z Chen","year":"2020","unstructured":"Chen Z, Wang Y, Zhao B, Cheng J, Zhao X, Duan Z (2020) Knowledge graph completion: a review. Ieee Access 8:192435\u2013192456","journal-title":"Ieee Access"},{"key":"292_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13042-024-02106-6","volume":"15","author":"C Chen","year":"2024","unstructured":"Chen C, Zheng F, Cui J, Cao Y, Liu G, Wu J, Zhou J (2024) Survey and open problems in privacy-preserving knowledge graph: merging, query, representation, completion, and applications. Int J Machine Learn Cybern 15:1\u201320","journal-title":"Int J Machine Learn Cybern"},{"key":"292_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2021.100696","volume":"72","author":"X Han","year":"2022","unstructured":"Han X, Dell\u2019Aglio D, Grubenmann T, Cheng R, Bernstein A (2022) A framework for differentially-private knowledge graph embeddings. J Web Semantics 72:100696","journal-title":"J Web Semantics"},{"key":"292_CR10","doi-asserted-by":"crossref","unstructured":"Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R, et al (2021) Advances and open problems in federated learning. Foundations and trends\u00ae in machine learning 14(1\u20132), 1\u2013210","DOI":"10.1561\/2200000083"},{"issue":"4","key":"292_CR11","doi-asserted-by":"publisher","first-page":"3347","DOI":"10.1109\/TKDE.2021.3124599","volume":"35","author":"Q Li","year":"2021","unstructured":"Li Q, Wen Z, Wu Z, Hu S, Wang N, Li Y, Liu X, He B (2021) A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Trans Knowl Data Eng 35(4):3347\u20133366","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"292_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775","volume":"216","author":"C Zhang","year":"2021","unstructured":"Zhang C, Xie Y, Bai H, Yu B, Li W, Gao Y (2021) A survey on federated learning. Knowl-Based Syst 216:106775","journal-title":"Knowl-Based Syst"},{"key":"292_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854","volume":"149","author":"L Li","year":"2020","unstructured":"Li L, Fan Y, Tse M, Lin K-Y (2020) A review of applications in federated learning. Comput Ind Eng 149:106854","journal-title":"Comput Ind Eng"},{"key":"292_CR14","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282 . PMLR"},{"key":"292_CR15","doi-asserted-by":"crossref","unstructured":"Chen M, Zhang W, Yuan Z, Jia Y, Chen H (2021) Fede: embedding knowledge graphs in federated setting. In: Proceedings of the 10th International Joint Conference on Knowledge Graphs, pp. 80\u201388","DOI":"10.1145\/3502223.3502233"},{"key":"292_CR16","doi-asserted-by":"crossref","unstructured":"Zhang K, Wang Y, Wang H, Huang L, Yang C, Chen X, Sun L (2022) Efficient federated learning on knowledge graphs via privacy-preserving relation embedding aggregation. arXiv preprint arXiv:2203.09553","DOI":"10.18653\/v1\/2022.findings-emnlp.43"},{"key":"292_CR17","doi-asserted-by":"crossref","unstructured":"Peng H, Li H, Song Y, Zheng V, Li J (2021) Differentially private federated knowledge graphs embedding. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1416\u20131425","DOI":"10.1145\/3459637.3482252"},{"key":"292_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109459","volume":"252","author":"M Chen","year":"2022","unstructured":"Chen M, Zhang W, Yuan Z, Jia Y, Chen H (2022) Federated knowledge graph completion via embedding-contrastive learning. Knowl-Based Syst 252:109459","journal-title":"Knowl-Based Syst"},{"key":"292_CR19","first-page":"2444","volume":"2023","author":"X Zhu","year":"2023","unstructured":"Zhu X, Li G, Hu W (2023) Heterogeneous federated knowledge graph embedding learning and unlearning. Proceed ACM Web Conf 2023:2444\u20132454","journal-title":"Proceed ACM Web Conf"},{"key":"292_CR20","doi-asserted-by":"crossref","unstructured":"Li Q, Diao Y, Chen Q, He B (2022) Federated learning on non-iid data silos: an experimental study. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 965\u2013978 . IEEE","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"292_CR21","unstructured":"Li X, Huang K, Yang W, Wang S, Zhang Z (2019) On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189"},{"key":"292_CR22","doi-asserted-by":"crossref","unstructured":"Meng W, Chen S, Feng Z (2021) Federated knowledge graph embeddings with heterogeneous data. In: Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction: 6th China Conference, CCKS 2021, Guangzhou, China, November 4-7, 2021, Proceedings 6, pp. 16\u201326 . Springer","DOI":"10.1007\/978-981-16-6471-7_2"},{"key":"292_CR23","unstructured":"Yang B, Yih W-t, He X, Gao J, Deng L (2014) Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575"},{"key":"292_CR24","first-page":"3065","volume":"34","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Cai J, Zhang Y, Wang J (2020) Learning hierarchy-aware knowledge graph embeddings for link prediction. Proceed AAAI Conf Artific Intell 34:3065\u20133072","journal-title":"Proceed AAAI Conf Artific Intell"},{"key":"292_CR25","unstructured":"Hinton G (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531"},{"key":"292_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112290","volume":"301","author":"Y Wang","year":"2024","unstructured":"Wang Y, Wang H, Liu X, Yan Y (2024) Gfedkg: Gnn-based federated embedding model for knowledge graph completion. Knowl-Based Syst 301:112290","journal-title":"Knowl-Based Syst"},{"key":"292_CR27","unstructured":"Zou H, Kim ZM, Kang D (2023) A survey of diffusion models in natural language processing. arXiv preprint arXiv:2305.14671"},{"key":"292_CR28","first-page":"36479","volume":"35","author":"C Saharia","year":"2022","unstructured":"Saharia C, Chan W, Saxena S, Li L, Whang J, Denton EL, Ghasemipour K, Gontijo Lopes R, Karagol Ayan B, Salimans T et al (2022) Photorealistic text-to-image diffusion models with deep language understanding. Adv Neural Inf Process Syst 35:36479\u201336494","journal-title":"Adv Neural Inf Process Syst"},{"key":"292_CR29","first-page":"56998","volume":"36","author":"J Lovelace","year":"2024","unstructured":"Lovelace J, Kishore V, Wan C, Shekhtman E, Weinberger KQ (2024) Latent diffusion for language generation. Adv Neural Inf Process Syst 36:56998","journal-title":"Adv Neural Inf Process Syst"},{"key":"292_CR30","first-page":"16693","volume":"36","author":"I Gulrajani","year":"2024","unstructured":"Gulrajani I, Hashimoto TB (2024) Likelihood-based diffusion language models. Adv Neural Inf Process Syst 36:16693","journal-title":"Adv Neural Inf Process Syst"},{"key":"292_CR31","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840\u20136851","journal-title":"Adv Neural Inf Process Syst"},{"key":"292_CR32","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal P, Nichol A (2021) Diffusion models beat gans on image synthesis. Adv Neural Inf Process Syst 34:8780\u20138794","journal-title":"Adv Neural Inf Process Syst"},{"key":"292_CR33","unstructured":"Devlin J (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"292_CR34","doi-asserted-by":"crossref","unstructured":"Xu T, Zhang P, Huang Q, Zhang H, Gan Z, Huang X, He X (2018) Attngan: fine-grained text to image generation with attentional generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1316\u20131324","DOI":"10.1109\/CVPR.2018.00143"},{"issue":"8","key":"292_CR35","doi-asserted-by":"publisher","first-page":"396","DOI":"10.3390\/info13080396","volume":"13","author":"M Zamini","year":"2022","unstructured":"Zamini M, Reza H, Rabiei M (2022) A review of knowledge graph completion. Information 13(8):396","journal-title":"Information"},{"key":"292_CR36","first-page":"2020","volume":"2024","author":"X Long","year":"2024","unstructured":"Long X, Zhuang L, Li A, Li H, Wang S (2024) Fact embedding through diffusion model for knowledge graph completion. Proceed ACM on Web Conf 2024:2020\u20132029","journal-title":"Proceed ACM on Web Conf"},{"key":"292_CR37","unstructured":"Ng A, Jordan M, Weiss Y (2001) On spectral clustering: Analysis and an algorithm. Adv Neural Inf Process Syst14"},{"key":"292_CR38","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Adv Neural Inf Process Syst 26"},{"key":"292_CR39","unstructured":"Trouillon T, Welbl J, Riedel S, Gaussier \u00c9, Bouchard G (2016) Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071\u20132080 . PMLR"},{"key":"292_CR40","unstructured":"Sun Z, Deng Z-H, Nie J-Y, Tang J (2019) Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197"},{"key":"292_CR41","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"292_CR42","doi-asserted-by":"crossref","unstructured":"Sui D, Chen Y, Zhao J, Jia Y, Xie Y, Sun W (2020) Feded: federated learning via ensemble distillation for medical relation extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2118\u20132128","DOI":"10.18653\/v1\/2020.emnlp-main.165"}],"container-title":["Data Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-025-00292-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41019-025-00292-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-025-00292-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T08:41:44Z","timestamp":1765528904000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41019-025-00292-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,10]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["292"],"URL":"https:\/\/doi.org\/10.1007\/s41019-025-00292-z","relation":{},"ISSN":["2364-1185","2364-1541"],"issn-type":[{"value":"2364-1185","type":"print"},{"value":"2364-1541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,10]]},"assertion":[{"value":"1 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All involved bibliographies are properly cited. The submission does not infringe any intellectual property rights (including without limitation copyright, database rights or trademark rights) or other third party rights and no license from or payments to a third-party are required to publish the submission. If the submission contains materials from other sources (e.g. illustrations, tables, text quotations), the\u00a0Author(s) have obtained written permissions to the extent necessary from the copyright holder(s). Otherwise,\u00a0the Author(s) of the submission shall take the blame for the violation or infringement of the related copyright and indemnify KBS for all its losses incurred. No data have been fabricated or manipulated (including images) to support the conclusion of the submission.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}]}}