{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T14:01:14Z","timestamp":1782741674099,"version":"3.54.5"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00521-024-10969-7","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T16:00:46Z","timestamp":1736956846000},"page":"6515-6526","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Fairness-driven federated learning-based spam email detection using clustering techniques"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2807-1869","authenticated-orcid":false,"given":"Vishal","family":"Kaushal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangeeta","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,15]]},"reference":[{"key":"10969_CR1","unstructured":"Tankovska H Statista-number of e-mail users worldwide from 2017 to 2024"},{"issue":"2","key":"10969_CR2","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1007\/s10462-022-10195-4","volume":"56","author":"F J\u00e1\u00f1ez-Martino","year":"2023","unstructured":"J\u00e1\u00f1ez-Martino F, Alaiz-Rodr\u00edguez R, Gonz\u00e1lez-Castro V, Fidalgo E, Alegre E (2023) A review of spam email detection: analysis of spammer strategies and the dataset shift problem. Artif Intell Rev 56(2):1145\u20131173","journal-title":"Artif Intell Rev"},{"key":"10969_CR3","doi-asserted-by":"crossref","unstructured":"Kriegeskorte N (2015) Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 417\u2013446","DOI":"10.1146\/annurev-vision-082114-035447"},{"key":"10969_CR4","doi-asserted-by":"crossref","unstructured":"Singh M, Pamula R, et al (2018) Email spam classification by support vector machine. In: 2018 International Conference on Computing, Power and Communication Technologies (GUCON), pp 878\u2013882. IEEE","DOI":"10.1109\/GUCON.2018.8674973"},{"key":"10969_CR5","unstructured":"Androutsopoulos I, Koutsias J, Chandrinos KV, Spyropoulos CD (2000) An evaluation of naive bayesian anti-spam filtering. In: Proceedings of the Workshop on Machine Learning in the New Information Age, pp 9\u201317"},{"key":"10969_CR6","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1016\/j.procs.2021.03.107","volume":"184","author":"Q Yaseen","year":"2021","unstructured":"Yaseen Q (2021) Spam email detection using deep learning techniques. Procedia Computer Science 184:853\u2013858","journal-title":"Procedia Computer Science"},{"key":"10969_CR7","first-page":"1805","volume":"15","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Zhang L, Zhang Z, Chen Z, Yang Q (2020) A deep learning approach to spam email detection. IEEE Trans Inf Forensics Secur 15:1805\u20131817","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10969_CR8","doi-asserted-by":"crossref","unstructured":"Ta\u00efk A, Cherkaoui S (2020) Electrical load forecasting using edge computing and federated learning. In: ICC 2020-2020 IEEE International Conference on Communications (ICC), pp 1\u20136","DOI":"10.1109\/ICC40277.2020.9148937"},{"key":"10969_CR9","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"},{"issue":"7","key":"10969_CR10","doi-asserted-by":"publisher","first-page":"5476","DOI":"10.1109\/JIOT.2020.3030072","volume":"8","author":"S AbdulRahman","year":"2020","unstructured":"AbdulRahman S, Tout H, Ould-Slimane H, Mourad A, Talhi C, Guizani M (2020) A survey on federated learning: The journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J 8(7):5476\u20135497","journal-title":"IEEE Internet Things J"},{"key":"10969_CR11","doi-asserted-by":"crossref","unstructured":"Kaushal V, Sharma S (2022) Some observations on the behaviour of federated learning. Federated Learning for IoT Applications 67\u201374","DOI":"10.1007\/978-3-030-85559-8_5"},{"key":"10969_CR12","unstructured":"Li C, Wang B, Wang T, Chen H, Jin X (2021) Federated learning for spam email detection. IEEE Transactions on Dependable and Secure Computing 1\u20131"},{"key":"10969_CR13","unstructured":"Chen M, Wang Y, Kishore RM, Franklin MJ, Li T (2019) Deeptest: Automated testing of deep-neural-network-driven autonomous cars. In: 2019 IEEE 33rd International Conference on Data Engineering (ICDE), pp 1532\u20131543"},{"key":"10969_CR14","unstructured":"Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Vepakomma P (2019) Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046"},{"key":"10969_CR15","doi-asserted-by":"crossref","unstructured":"Kaddoura S, Alfandi O, Dahmani N (2020) A spam email detection mechanism for english language text emails using deep learning approach. In: 2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp 193\u2013198. IEEE","DOI":"10.1109\/WETICE49692.2020.00045"},{"key":"10969_CR16","unstructured":"Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R et al. (2019) Advances and open problems in federated learning. Available: https:\/\/arxiv.org\/abs\/1912.04977"},{"key":"10969_CR17","unstructured":"Sahami M, Dumais S, Heckerman D, Horvitz E (1998) A bayesian approach to filtering junk e-mail. In: Learning for Text Categorization: Papers from the 1998 Workshop, vol. 62, pp 98\u2013105"},{"key":"10969_CR18","unstructured":"Androutsopoulos I, Koutsias J, Chandrinos KV, Spyropoulos CD (2000) An evaluation of naive bayesian anti-spam filtering. In: Proceedings of the Workshop on Machine Learning in the New Information Age, pp 9\u201317"},{"key":"10969_CR19","doi-asserted-by":"crossref","unstructured":"Bahgat E, Rady S, Gad W, Moawad I (2018) Efficient email classification approach based on semantic methods. Ain Shams Engineering Journal, 3259\u20133269","DOI":"10.1016\/j.asej.2018.06.001"},{"key":"10969_CR20","doi-asserted-by":"crossref","unstructured":"Srinivasan S, Ravi V, Alazab M, Ketha S, Ala\u2019M A-Z, Padannayil SK (2021) Spam emails detection based on distributed word embedding with deep learning. In: Machine Intelligence and Big Data Analytics for Cybersecurity Applications, pp 161\u2013189. Springer, ???","DOI":"10.1007\/978-3-030-57024-8_7"},{"key":"10969_CR21","doi-asserted-by":"crossref","unstructured":"Hassanpour R, Dogdu E, Choupani R, Goker O, Nazli N (2018) Phishing e-mail detection by using deep learning algorithms. In: Proceedings of the ACMSE 2018 Conference, pp 1\u20131","DOI":"10.1145\/3190645.3190719"},{"key":"10969_CR22","doi-asserted-by":"crossref","unstructured":"Egozi G, Verma R (2018) Phishing email detection using robust nlp techniques. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp 7\u201312","DOI":"10.1109\/ICDMW.2018.00009"},{"key":"10969_CR23","unstructured":"Sharma A, Sahni S (2011) A comparative study of classification algorithms for spam email data analysis. International Journal on Computer Science and Engineering 1890\u20131895"},{"key":"10969_CR24","doi-asserted-by":"crossref","unstructured":"Zhang H, Li D (2007) Naive bayes text classifier. In: 2007 IEEE International Conference on Granular Computing, pp 708\u2013708","DOI":"10.1109\/GrC.2007.40"},{"issue":"5","key":"10969_CR25","first-page":"74","volume":"2","author":"AN Soni","year":"2019","unstructured":"Soni AN (2019) Spam-e-mail-detection-using-advanced-deep-convolution-neuralnetwork-algorithms. JOURNAL FOR INNOVATIVE DEVELOPMENT IN PHARMACEUTICAL AND TECHNICAL SCIENCE 2(5):74\u201380","journal-title":"JOURNAL FOR INNOVATIVE DEVELOPMENT IN PHARMACEUTICAL AND TECHNICAL SCIENCE"},{"key":"10969_CR26","doi-asserted-by":"crossref","unstructured":"Seth S, Biswas S (2017) Multimodal spam classification using deep learning techniques. In: 2017 13th International Conference on SignalImage Technology Internet-Based Systems (SITIS), pp 346\u2013349","DOI":"10.1109\/SITIS.2017.91"},{"key":"10969_CR27","doi-asserted-by":"crossref","unstructured":"Ezpeleta E, Zurutuza U, Hidalgo JMG (2016) Does sentiment analysis help in bayesian spam filtering? In: International Conference on Hybrid Artificial Intelligence Systems, pp 79\u201390. Springer, ???","DOI":"10.1007\/978-3-319-32034-2_7"},{"issue":"2","key":"10969_CR28","doi-asserted-by":"publisher","first-page":"73","DOI":"10.17706\/jcp.15.2.73-84","volume":"15","author":"A Bibi","year":"2020","unstructured":"Bibi A, Latif R, Khalid S, Ahmed W, Shabir RA, Shahryar T (2020) Spam mail scanning using machine learning algorithm. JCP 15(2):73\u201384","journal-title":"JCP"},{"key":"10969_CR29","unstructured":"Sun Y, Chong N, Ochiai H (2021) Privacy-preserving phishing email detection based on federated learning and lstm. arXiv preprint arXiv:2110.06025"},{"issue":"9","key":"10969_CR30","doi-asserted-by":"publisher","first-page":"4346","DOI":"10.3390\/s23094346","volume":"23","author":"C Thapa","year":"2023","unstructured":"Thapa C, Tang JW, Abuadbba A, Gao Y, Camtepe S, Nepal S, Almashor M, Zheng Y (2023) Evaluation of federated learning in phishing email detection. Sensors 23(9):4346","journal-title":"Sensors"},{"key":"10969_CR31","unstructured":"Cho YJ, Wang J, Joshi G (2020) Client selection in federated learning: Convergence analysis and power-of-choice selection strategies. arXiv preprint arXiv:2010.01243"},{"issue":"2","key":"10969_CR32","first-page":"1035","volume":"10","author":"Y Zhan","year":"2021","unstructured":"Zhan Y, Zhang J, Hong Z, Wu L, Li P, Guo S (2021) A survey of incentive mechanism design for federated learning. IEEE Trans Emerg Top Comput 10(2):1035\u20131044","journal-title":"IEEE Trans Emerg Top Comput"},{"issue":"2","key":"10969_CR33","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1007\/s10462-022-10195-4","volume":"56","author":"A Nilsson","year":"2023","unstructured":"Nilsson A, Smith S, Ulm G, Gustavsson E, Jirstrand M (2023) A review of spam email detection: analysis of spammer strategies and the dataset shift problem. Artif Intell Rev 56(2):1145\u20131173","journal-title":"Artif Intell Rev"},{"key":"10969_CR34","unstructured":"Sun C, Huang L, Qiu X (2019) Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence. arXiv preprint arXiv:1903.09588"},{"key":"10969_CR35","unstructured":"Flower Library: Available - https:\/\/flower.dev\/"},{"key":"10969_CR36","unstructured":"Tensor Flow: https:\/\/www.tensorflow.org\/"},{"key":"10969_CR37","unstructured":"Enron email dataset. Available: https:\/\/www.cs.cmu.edu\/enron\/"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10969-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10969-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10969-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T17:35:38Z","timestamp":1741282538000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10969-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,15]]},"references-count":37,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10969"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10969-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,15]]},"assertion":[{"value":"7 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The research presented in this article did not involve studies with human participants or animals performed by any of the authors. Therefore, ethical approval from an institutional review board or ethics committee was not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Vishal Kaushal and Sangeeta Sharma declare that they have no conflict of interest that could influence the interpretation or presentation of the research findings. There are no financial relationships, employment affiliations, or personal connections that could be perceived as a potential conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}