{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T15:47:35Z","timestamp":1769269655825,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s10489-024-06211-5","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T07:54:38Z","timestamp":1736841278000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Personalized federated knowledge graph embedding with client-wise relation graph"],"prefix":"10.1007","volume":"55","author":[{"given":"Xiaoxiong","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Zhiwei","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Dusit","family":"Niyato","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7626-7295","authenticated-orcid":false,"given":"ZhiQi","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"issue":"9","key":"6211_CR1","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.3390\/sym11091096","volume":"11","author":"J Ma","year":"2019","unstructured":"Ma J, Qiao Y, Hu G, Wang Y, Zhang C, Huang Y, Sangaiah AK, Wu H, Zhang H, Ren K (2019) Elpkg: A high-accuracy link prediction approach for knowledge graph completion. Symmetry. 11(9):1096","journal-title":"Symmetry."},{"key":"6211_CR2","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.websem.2017.06.002","volume":"44","author":"I Abdelaziz","year":"2017","unstructured":"Abdelaziz I, Fokoue A, Hassanzadeh O, Zhang P, Sadoghi M (2017) Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions. Journal of Web Semantics. 44:104\u2013117","journal-title":"Journal of Web Semantics."},{"key":"6211_CR3","doi-asserted-by":"crossref","unstructured":"Wang X, Liu K, Wang D, Wu L, Fu Y, Xie X (2022) Multi-level recommendation reasoning over knowledge graphs with reinforcement learning. In: Proceedings of the ACM web conference 2022, pp 2098\u20132108","DOI":"10.1145\/3485447.3512083"},{"key":"6211_CR4","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 (vol 1: Long Papers), pp 221\u2013231","DOI":"10.18653\/v1\/P17-1021"},{"key":"6211_CR5","unstructured":"Regulation E (2016) 679 of the european parliament and of the council on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95\/46\/ec (general data protection regulation). EC (General Data Protection Regulation)"},{"key":"6211_CR6","doi-asserted-by":"crossref","unstructured":"Chen M, Zhang W, Yuan Z, Jia Y, Chen H (2021) Fede: Embedding knowledge graphs in federated setting. In: The 10th international joint conference on knowledge graphs, pp 80\u201388","DOI":"10.1145\/3502223.3502233"},{"key":"6211_CR7","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":"6211_CR8","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":"6211_CR9","doi-asserted-by":"crossref","unstructured":"Zhang J, Hua Y, Wang H, Song T, Xue Z, Ma R, Guan H (2023) Fedala: Adaptive local aggregation for personalized federated learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 11237\u201311244","DOI":"10.1609\/aaai.v37i9.26330"},{"key":"6211_CR10","doi-asserted-by":"crossref","unstructured":"Chen F, Long G, Wu Z, Zhou T, Jiang J (2022) Personalized federated learning with a graph. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. Int Joint Conf Artif Intell","DOI":"10.24963\/ijcai.2022\/357"},{"key":"6211_CR11","unstructured":"Ye R, Ni Z, Wu F, Chen S, Wang Y (2023) Personalized federated learning with inferred collaboration graphs. In: International conference on machine learning, PMLR, pp 39801\u201339817"},{"issue":"1","key":"6211_CR12","doi-asserted-by":"publisher","first-page":"21839","DOI":"10.1038\/s41598-024-72748-7","volume":"14","author":"C Hausleitner","year":"2024","unstructured":"Hausleitner C, Mueller H, Holzinger A, Pfeifer B (2024) Collaborative weighting in federated graph neural networks for disease classification with the human-in-the-loop. Sci Rep 14(1):21839","journal-title":"Sci Rep"},{"key":"6211_CR13","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, PMLR, pp 1273\u20131282"},{"key":"6211_CR14","doi-asserted-by":"crossref","unstructured":"Li Q, He B, Song D (2021) Model-contrastive federated learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10713\u201310722","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"6211_CR15","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. In: Findings of the association for computational linguistics: EMNLP 2022, pp 613\u2013621","DOI":"10.18653\/v1\/2022.findings-emnlp.43"},{"key":"6211_CR16","unstructured":"Lample G, Conneau A, Ranzato M, Denoyer L, J\u00e9gou H (2018) Word translation without parallel data. In: International conference on learning representations"},{"issue":"11","key":"6211_CR17","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"6211_CR18","doi-asserted-by":"crossref","unstructured":"Cheng S, Zhang N, Tian B, Chen X, Liu, Q, Chen H (2024) Editing language model-based knowledge graph embeddings. In: Proceedings of the AAAI conference on artificial intelligence, vol 38, pp 17835\u201317843","DOI":"10.1609\/aaai.v38i16.29737"},{"key":"6211_CR19","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Adv Neural Inform Process Syst 26"},{"key":"6211_CR20","unstructured":"Sun Z, Deng Z-H, Nie J-Y, Tang J (2018) Rotate: Knowledge graph embedding by relational rotation in complex space. In: International conference on learning representations"},{"key":"6211_CR21","unstructured":"Fan M, Zhou Q, Chang E, Zheng F (2014) Transition-based knowledge graph embedding with relational mapping properties. In: Proceedings of the 28th pacific asia conference on language, information and computing, pp 328\u2013337"},{"key":"6211_CR22","doi-asserted-by":"crossref","unstructured":"Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence, pp 2181\u20132187","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"6211_CR23","unstructured":"Yang B, Yih SW-t, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the international conference on learning representations (ICLR) 2015"},{"key":"6211_CR24","unstructured":"Trouillon T, Welbl J, Riedel S, Gaussier \u00c9, Bouchard G (2016) Complex embeddings for simple link prediction. In: International conference on machine learning, PMLR, pp 2071\u20132080"},{"key":"6211_CR25","unstructured":"Nickel M, Tresp V, Kriegel H- et al (2011) A three-way model for collective learning on multi-relational data. In: Icml, pp 3104482\u20133104584"},{"key":"6211_CR26","doi-asserted-by":"crossref","unstructured":"Nickel M, Rosasco L, Poggio T (2016) Holographic embeddings of knowledge graphs. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.10314"},{"key":"6211_CR27","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Van Den\u00a0Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: The semantic web: 15th international conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings 15, Springer, pp 593\u2013607","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"6211_CR28","unstructured":"Vashishth S, Sanyal S, Nitin V, Talukdar P (2020) Composition-based multi-relational graph convolutional networks. In: International conference on learning representations"},{"key":"6211_CR29","doi-asserted-by":"crossref","unstructured":"Dettmers T, Minervini P, Stenetorp P, Riedel S (2018) Convolutional 2d knowledge graph embeddings. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"6211_CR30","doi-asserted-by":"crossref","unstructured":"Pan S, Luo L, Wang Y, Chen C, Wang J, Wu X (2024) Unifying large language models and knowledge graphs: A roadmap. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2024.3352100"},{"key":"6211_CR31","unstructured":"Yao L, Mao C, Luo Y (2019) Kg-bert: Bert for knowledge graph completion. arXiv preprint arXiv:1909.03193"},{"key":"6211_CR32","doi-asserted-by":"crossref","unstructured":"Wang B, Shen T, Long G, Zhou T, Wang Y, Chang Y (2021) Structure-augmented text representation learning for efficient knowledge graph completion. In: Proceedings of the web conference 2021, pp 1737\u20131748","DOI":"10.1145\/3442381.3450043"},{"key":"6211_CR33","doi-asserted-by":"crossref","unstructured":"Lv X, Lin Y, Cao Y, Hou L, Li J, Liu Z, Li P, Zhou J (2022) Do pre-trained models benefit knowledge graph completion? a reliable evaluation and a reasonable approach. Assoc Comput Linguist","DOI":"10.18653\/v1\/2022.findings-acl.282"},{"key":"6211_CR34","doi-asserted-by":"crossref","unstructured":"Xie X, Zhang N, Li Z, Deng S, Chen H, Xiong F, Chen M, Chen H (2022) From discrimination to generation: Knowledge graph completion with generative transformer. In: Companion proceedings of the web conference 2022, pp 162\u2013165","DOI":"10.1145\/3487553.3524238"},{"key":"6211_CR35","unstructured":"Chen C, Wang Y, Li B, Lam K-Y (2022) Knowledge is flat: A seq2seq generative framework for various knowledge graph completion. In: Proceedings of the 29th international conference on computational linguistics, pp 4005\u20134017"},{"key":"6211_CR36","doi-asserted-by":"crossref","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 Mach Learn Cybern pp 1\u201320","DOI":"10.1007\/s13042-024-02106-6"},{"key":"6211_CR37","doi-asserted-by":"crossref","unstructured":"Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, Jin S, Quek TQS, Vincent\u00a0Poor H (2020) Federated learning with differential privacy: Algorithms and performance analysis. IEEE Trans Inform Forensic Sec 15:3454\u20133469","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"6211_CR38","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V (2020) Federated optimization in heterogeneous networks. Proc Mach Learn Syst 2:429\u2013450","journal-title":"Proc Mach Learn Syst"},{"key":"6211_CR39","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"6211_CR40","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06211-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06211-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06211-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T17:21:40Z","timestamp":1740244900000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06211-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,14]]},"references-count":40,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["6211"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06211-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,14]]},"assertion":[{"value":"15 December 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"This study affirms the absence of any potential conflicts of interest. All procedures involving human participants were conducted in strict accordance with ethical standards. The study did not involve animal participants. Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}}],"article-number":"318"}}