{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T09:56:34Z","timestamp":1772963794847,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T00:00:00Z","timestamp":1753574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Prince Sattam bin Abdulaziz University","award":["PSAU\/2024\/01\/29889"],"award-info":[{"award-number":["PSAU\/2024\/01\/29889"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Federated Learning (FL) is a privacy-focused technique for training models; however, most existing unlearning techniques in FL fall significantly short of the efficiency and situational awareness required by the GDPR. The paper introduces two new unlearning methods: EG-FedUnlearn, a gradient-based technique that eliminates the effect of specific target clients without retraining, and OFU-Ontology, an ontology-based approach that ranks data importance to facilitate forgetting contextually. EG-FedUnlearn directly eliminates the contributions of specific target data by reversing the gradient, whereas OFU-Ontology utilizes semantic relevance to prioritize forgetting data of the least importance, thereby minimizing the unlearning-induced degradation of models. The results of experiments on seven benchmark datasets demonstrate the good performance of both algorithms. OFU-Ontology yields 98% accuracy of unlearning while maintaining high model utility with very limited accuracy loss under class-based deletion on MNIST (e.g., 95%), surpassing FedEraser and VeriFi on the multiple metrics of residual influence, communication overhead, and computational cost. These results indicate that the cooperation of efficient unlearning algorithms with semantic reasoning, minimized unlearning costs, and operational performance in a distributed environment. This paper becomes the first to incorporate ontological knowledge into federated unlearning, thereby opening new avenues for scalable and intelligent private machine learning systems.<\/jats:p>","DOI":"10.3390\/fi17080335","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T08:51:33Z","timestamp":1753692693000},"page":"335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Privacy-Preserving Federated Unlearning with Ontology-Guided Relevance Modeling for Secure Distributed Systems"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7145-5617","authenticated-orcid":false,"given":"Naglaa E.","family":"Ghannam","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia"},{"name":"Department of Mathematics, Faculty of Science, Al-Azhar University (Girls\u2019 Branch), Cairo 11754, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9042-248X","authenticated-orcid":false,"given":"Esraa A.","family":"Mahareek","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Al-Azhar University (Girls\u2019 Branch), Cairo 11754, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,27]]},"reference":[{"key":"ref_1","unstructured":"(General Data Protection Regulation (GDPR), 2018). General Data Protection Regulation (GDPR)."},{"key":"ref_2","unstructured":"(2025, June 25). California Consumer Privacy Act (CCPA), California Legislative Information, Available online: https:\/\/leginfo.legislature.ca.gov."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2430","DOI":"10.1109\/JSAC.2020.3000372","article-title":"Analyzing User-Level Privacy Attack Against Federated Learning","volume":"38","author":"Song","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_4","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A. (2017, January 20\u201322). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, USA."},{"key":"ref_5","unstructured":"Ginart, A.A., Guan, M.Y., Valiant, G., and Zou, J. (2019, January 8\u201314). Making AI Forget You: Data Deletion in Machine Learning. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, G., Ma, X., Yang, Y., Wang, C., and Liu, J. (2021, January 25\u201328). FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models. Proceedings of the 2021 IEEE\/ACM 29th International Symposium on Quality of Service (IWQOS), Tokyo, Japan.","DOI":"10.1109\/IWQOS52092.2021.9521274"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/MNET.001.2200198","article-title":"Federated Unlearning: Guarantee the Right of Clients to Forget","volume":"36","author":"Wu","year":"2022","journal-title":"IEEE Netw."},{"key":"ref_8","unstructured":"Xie, C., Huang, K., Chen, P.-Y., and Li, B. (2020, January 26\u201330). DBA: Distributed Backdoor Attacks Against Federated Learning. Proceedings of the International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia."},{"key":"ref_9","unstructured":"Liu, G., Ma, X., Yang, Y., Wang, C., and Liu, J. (2021). Federated Unlearning. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Staab, S., and Studer, R. (2009). What Is an Ontology?. Handbook on Ontologies, Springer. [2nd ed.].","DOI":"10.1007\/978-3-540-92673-3"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1007\/s10796-021-10180-2","article-title":"Ontology-Based Approach for the Measurement of Privacy Disclosure","volume":"24","author":"Zhu","year":"2022","journal-title":"Inf. Syst. Front."},{"key":"ref_12","unstructured":"Kairouz, P., Brendan, H., and Brendan, A. (2021). Advances and Open Problems in Federated Learning, Now Foundations and Trends."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., and Zhou, Y. (2019, January 11\u201315). A Hybrid Approach to Privacy-Preserving Federated Learning. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, London, UK.","DOI":"10.1145\/3338501.3357370"},{"key":"ref_14","first-page":"2","article-title":"A Survey on Federated Unlearning: Challenges, Methods, and Future Directions","volume":"57","author":"Liu","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1006\/knac.1993.1008","article-title":"A translation approach to portable ontology specifications","volume":"5","author":"Gruber","year":"1993","journal-title":"Knowl. Acquis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13046","DOI":"10.1109\/TNNLS.2023.3266233","article-title":"Fast Yet Effective Machine Unlearning","volume":"35","author":"Tarun","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_17","unstructured":"LeCun, Y., Cortes, C., and Burges, C.J.C. (2024, November 12). MNIST Handwritten Digit Database. AT&T Labs. Available online: http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"ref_18","unstructured":"Krizhevsky, A., and Hinton, G. (2024, September 28). Learning Multiple Layers of Features from Tiny Images. Available online: https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html."},{"key":"ref_19","unstructured":"Dua, D., and Graff, C. (2019). UCI Machine Learning Repository: Adult Data Set, University of California, Irvine."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5866","DOI":"10.1016\/j.eswa.2008.07.018","article-title":"Knowledge discovery on RFM model using Bernoulli sequence","volume":"36","author":"Yeh","year":"2009","journal-title":"Expert. Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_22","unstructured":"Stolfo, S.J., Fan, W., Lee, W., Prodromidis, A.L., and Chan, P.K. (2000, January 25\u201327). Cost-based modeling for fraud and intrusion detection: Results from the JAM project. Proceedings of the DARPA Information Survivability Conference and Exposition, DISCEX\u201900, Hilton Head, SC, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1609\/icwsm.v14i1.7347","article-title":"The Pushshift Reddit Dataset","volume":"Volume 14","author":"Baumgartner","year":"2020","journal-title":"Proceedings of the Fourteenth International AAAI Conference on Web and Social Media"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"88137","DOI":"10.1109\/ACCESS.2021.3090019","article-title":"Coded Machine Unlearning","volume":"9","author":"Aldaghri","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","first-page":"18075","article-title":"Remember what you want to forget: Algorithms for machine unlearning","volume":"34","author":"Sekhari","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1109\/TIFS.2023.3265506","article-title":"Zero-Shot Machine Unlearning","volume":"18","author":"Chundawat","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_27","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V. (2018). On the Convergence of Federated Optimization in Heterogeneous Networks. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gu, H., Zhu, G., Zhang, J., Zhao, X., Han, Y., Fan, L., and Yang, Q. (2024, January 3\u20139). Unlearning during Learning: An Efficient Federated Machine Unlearning Method. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24), Jeju, Republic of Korea. Available online: https:\/\/gdpr-info.eu\/art-17-gdpr\/.","DOI":"10.24963\/ijcai.2024\/446"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C.A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N. (2021, January 24\u201327). Machine unlearning. Proceedings of the IEEE Symposium on Security and Privacy, San Francisco, CA, USA.","DOI":"10.1109\/SP40001.2021.00019"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/8\/335\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:16:51Z","timestamp":1760033811000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/8\/335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,27]]},"references-count":29,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["fi17080335"],"URL":"https:\/\/doi.org\/10.3390\/fi17080335","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,27]]}}}