{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T01:21:07Z","timestamp":1777944067676,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:00:00Z","timestamp":1777680000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:00:00Z","timestamp":1777680000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21431-2","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:46:33Z","timestamp":1777707993000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Genetic algorithm-driven aggregation for federated learning in 6G-enabled smart cities"],"prefix":"10.1007","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8436-3580","authenticated-orcid":false,"given":"Rached","family":"Fouda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salah","family":"Zidi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Najoua","family":"Bennaji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Issam","family":"Zidi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,2]]},"reference":[{"key":"21431_CR1","unstructured":"Samsung Research (2020) 6G White Paper: The Next Hyper-Connected Experience for All. https:\/\/research.samsung.com\/. Accessed 30 Nov 2023"},{"key":"#cr-split#-21431_CR2.1","unstructured":"European Union (2016) General Data Protection Regulation (GDPR), Regulation"},{"key":"#cr-split#-21431_CR2.2","unstructured":"(EU) 2016\/679. https:\/\/eur-lex.europa.eu\/eli\/reg\/2016\/679\/oj"},{"key":"21431_CR3","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"},{"issue":"1","key":"21431_CR4","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1038\/s41591-022-02155-w","volume":"29","author":"J Terrail","year":"2023","unstructured":"Terrail J, Leopold A, Joly C et al (2023) Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer. Nat Med 29(1):135\u2013146. https:\/\/doi.org\/10.1038\/s41591-022-02155-w","journal-title":"Nat Med"},{"key":"21431_CR5","doi-asserted-by":"crossref","unstructured":"Yang W, Zhang Y, Ye K, Li L, Xu C-Z (2019) Ffd: A federated learning based method for credit card fraud detection. In: Big Data-BigData 2019: 8th International congress, held as part of the services conference federation, SCF 2019, San Diego, CA, USA, June 25\u201330, 2019, Proceedings 8, pp 18\u201332. Springer","DOI":"10.1007\/978-3-030-23551-2_2"},{"key":"21431_CR6","doi-asserted-by":"publisher","first-page":"158","DOI":"10.3390\/network3010008","volume":"3","author":"MM Rashid","year":"2023","unstructured":"Rashid MM, Khan SU, Eusufzai F, Redwan MA, Sabuj SR, Elsharief M (2023) A federated learning-based approach for improving intrusion detection in industrial internet of things networks. Network 3:158\u2013179","journal-title":"Network"},{"issue":"3","key":"21431_CR7","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: Challenges, methods, and future directions. IEEE Signal Process Mag 37(3):50\u201360","journal-title":"IEEE Signal Process Mag"},{"key":"21431_CR8","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.future.2023.09.008","volume":"150","author":"P Qi","year":"2024","unstructured":"Qi P, Chiaro D, Guzzo A, Ianni M, Fortino G, Piccialli F (2024) Model aggregation techniques in federated learning: A comprehensive survey. Futur Gener Comput Syst 150:272\u2013293. https:\/\/doi.org\/10.1016\/j.future.2023.09.008","journal-title":"Futur Gener Comput Syst"},{"key":"21431_CR9","doi-asserted-by":"publisher","unstructured":"Jayaram KR, Muthusamy V, Thomas G, Verma A, Purcell M (2022) Adaptive aggregation for federated learning. In: 2022 IEEE International Conference on Big Data (Big Data), pp 180\u2013185. https:\/\/doi.org\/10.1109\/BigData55660.2022.10021119","DOI":"10.1109\/BigData55660.2022.10021119"},{"key":"21431_CR10","unstructured":"Bonawitz K (2019) Towards federated learning at scale: System design. arXiv:1902.01046"},{"issue":"8","key":"21431_CR11","doi-asserted-by":"publisher","first-page":"7751","DOI":"10.1109\/JIOT.2020.2991401","volume":"7","author":"Y Liu","year":"2020","unstructured":"Liu Y, Yu JJQ, Kang J, Niyato D, Zhang S (2020) Privacy-preserving traffic flow prediction: A federated learning approach. IEEE Internet Things J 7(8):7751\u20137763. https:\/\/doi.org\/10.1109\/JIOT.2020.2991401","journal-title":"IEEE Internet Things J"},{"issue":"12","key":"21431_CR12","doi-asserted-by":"publisher","first-page":"8464","DOI":"10.1109\/TII.2021.3055283","volume":"17","author":"C Zhang","year":"2021","unstructured":"Zhang C, Zhang S, James J, Yu S (2021) Fastgnn: A topological information protected federated learning approach for traffic speed forecasting. IEEE Trans Industr Inf 17(12):8464\u20138474","journal-title":"IEEE Trans Industr Inf"},{"key":"21431_CR13","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.ijmedinf.2018.01.007","volume":"112","author":"TS Brisimi","year":"2018","unstructured":"Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W (2018) Federated learning of predictive models from federated electronic health records. Int J Med Informatics 112:59\u201367. https:\/\/doi.org\/10.1016\/j.ijmedinf.2018.01.007","journal-title":"Int J Med Informatics"},{"key":"21431_CR14","unstructured":"Liu H, Zhang X, Shen X, Sun H (2021) A federated learning framework for smart grids: Securing power traces in collaborative learning. arXiv:2103.11870"},{"key":"21431_CR15","doi-asserted-by":"crossref","unstructured":"Kaur N, Gupta L (2025) Securing the 6g\u2013iot environment: A framework for enhancing transparency in artificial intelligence decision-making through explainable artificial intelligence. PubMed Central","DOI":"10.3390\/s25030854"},{"key":"21431_CR16","doi-asserted-by":"publisher","unstructured":"Abdel Hakeem SA et al (2022) Security requirements and challenges of 6g technologies and applications. PubMed Central https:\/\/doi.org\/10.3390\/s22051969","DOI":"10.3390\/s22051969"},{"key":"21431_CR17","unstructured":"Abouaomar A et al (2024) Optimizing energy and latency in 6g smart cities with edge cybertwins. arXiv preprint."},{"key":"21431_CR18","doi-asserted-by":"publisher","first-page":"103535","DOI":"10.1016\/j.comnet.2024.103535","volume":"253","author":"Y Chen","year":"2025","unstructured":"Rasheed I, Mostafa H (2025) Mobility-aware predictive split federated learning for 6g vehicular networks with ultra-low latency guarantees. Comput Netw 253:103535. https:\/\/doi.org\/10.1016\/j.comnet.2025.103535","journal-title":"Comput Netw"},{"key":"21431_CR19","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/j.ins.2023.03.033","volume":"632","author":"M Aly","year":"2023","unstructured":"Al-Huthaifi R, Li T, Huang W, Gu J, Li C (2023) Federated learning in smart cities: Privacy and security survey. Inf Sci 632:833\u2013857. https:\/\/doi.org\/10.1016\/j.ins.2023.03.033","journal-title":"Inf Sci"},{"key":"21431_CR20","doi-asserted-by":"publisher","unstructured":"Shenoy D et al (2025) Exploring privacy mechanisms and metrics in federated learning. Artif Intell Rev 58. https:\/\/doi.org\/10.1007\/s10462-025-11170-5","DOI":"10.1007\/s10462-025-11170-5"},{"key":"21431_CR21","doi-asserted-by":"publisher","DOI":"10.1145\/3704633","author":"Y Zhang","year":"2024","unstructured":"Bai L, Hu H, Ye Q, Li H, Wang L, Xu J (2024) Membership inference attacks and defenses in federated learning: A survey. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3704633","journal-title":"ACM Comput Surv"},{"key":"21431_CR22","doi-asserted-by":"publisher","unstructured":"Liu P et al (2022) Threats, attacks and defenses to federated learning: Issues, taxonomy and perspectives. Cybersecurity 5(6). https:\/\/doi.org\/10.1186\/s42400-021-00105-6","DOI":"10.1186\/s42400-021-00105-6"},{"key":"21431_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-025-11248-0","author":"W Yang","year":"2025","unstructured":"Yang W et al (2025) Deep learning model inversion attacks and defenses: A comprehensive survey. Artif Intell Rev. https:\/\/doi.org\/10.1007\/s10462-025-11248-0","journal-title":"Artif Intell Rev"},{"key":"21431_CR24","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. Proceed Mach Learn Syst 2:429\u2013450","journal-title":"Proceed Mach Learn Syst"},{"key":"21431_CR25","unstructured":"Karimireddy SP, Kale S, Mohri M, Reddi S, Stich S, Suresh AT (2020) Scaffold: Stochastic controlled averaging for federated learning. In: International conference on machine learning, pp 5132\u20135143. PMLR"},{"key":"21431_CR26","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":"21431_CR27","unstructured":"Acar DAE, Zhao Y, Navarro RM, Mattina M, Whatmough PN, Saligrama V (2021) Federated learning based on dynamic regularization. arXiv:2111.04263"},{"issue":"10","key":"21431_CR28","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.3390\/math12101601","volume":"12","author":"H Zhang","year":"2024","unstructured":"Zhang H, Zhang P, Hu M, Liu M, Wang J (2024) Fedub: Federated learning algorithm based on update bias. Mathematics 12(10):1601. https:\/\/doi.org\/10.3390\/math12101601","journal-title":"Mathematics"},{"issue":"4","key":"21431_CR29","doi-asserted-by":"publisher","first-page":"2708","DOI":"10.1109\/TNSE.2022.3168969","volume":"9","author":"L Tan","year":"2022","unstructured":"Tan L, Zhang X, Zhou Y, Che X, Hu M, Chen X, Wu D (2022) Adafed: optimizing participation-aware federated learning with adaptive aggregation weights. IEEE Trans Netw Sci Eng 9(4):2708\u20132720. https:\/\/doi.org\/10.1109\/TNSE.2022.3168969","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"21431_CR30","doi-asserted-by":"publisher","first-page":"23763","DOI":"10.1609\/aaai.v38i21.30557","volume":"38","author":"Y Tang","year":"2024","unstructured":"Tang Y (2024) Adapted weighted aggregation in federated learning. Proceed AAAI Conf Artif Intell 38:23763\u201323765. https:\/\/doi.org\/10.1609\/aaai.v38i21.30557","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"21431_CR31","doi-asserted-by":"crossref","unstructured":"Pan H, Durak G, Zhang Z, Taktak Y, Keles E, Aktas HE, Medetalibeyoglu A, Velichko Y, Spampinato C, Schoots I et al (2024) Adaptive aggregation weights for federated segmentation of pancreas mri. arXiv:2410.22530","DOI":"10.1109\/ISBI60581.2025.10981148"},{"key":"21431_CR32","unstructured":"Wu H, Wang P (2020) Fast-convergent federated learning with adaptive weighting. CoRR. arxiv:2012.00661"},{"key":"21431_CR33","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/978-3-031-09002-8_40","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"MI Khan","year":"2022","unstructured":"Khan MI, Jafaritadi M, Alhoniemi E, Kontio E, Khan SA (2022) Adaptive weight aggregation in federated learning for brain tumor segmentation. In: Crimi A, Bakas S (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer, Cham, pp 455\u2013469"},{"key":"21431_CR34","doi-asserted-by":"publisher","unstructured":"Ouyang C, Li Y, Mao J, Zhu D, Zhou C, Xu Z (2024) Enhancing federated learning with dynamic weight adjustment based on particle swarm optimization. Discov Comput 27. https:\/\/doi.org\/10.1007\/s10791-024-09478-x","DOI":"10.1007\/s10791-024-09478-x"},{"key":"21431_CR35","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1090.001.0001","volume-title":"Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology. Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA"},{"key":"21431_CR36","doi-asserted-by":"publisher","first-page":"12564","DOI":"10.1038\/s41598-025-96443-3","volume":"15","author":"L Toderean","year":"2025","unstructured":"Toderean L, Daian M, Cioara T, Anghel I, Antal M, Salomie I (2025) Heuristic based federated learning with adaptive hyperparameter tuning for households energy prediction. Sci Rep 15:12564. https:\/\/doi.org\/10.1038\/s41598-025-96443-3","journal-title":"Sci Rep"},{"key":"21431_CR37","doi-asserted-by":"publisher","first-page":"85489","DOI":"10.1109\/ACCESS.2023.3304368","volume":"11","author":"D Kang","year":"2023","unstructured":"Kang D, Ahn CW (2023) Ga approach to optimize training client set in federated learning. IEEE Access 11:85489\u201385500. https:\/\/doi.org\/10.1109\/ACCESS.2023.3304368","journal-title":"IEEE Access"},{"key":"21431_CR38","doi-asserted-by":"crossref","unstructured":"Guendouzi SB, Ouchani S, Malki M (2022) Genetic algorithm based aggregation for federated learning in industrial cyber physical systems. In: Computational intelligence in security for information systems conference, pp 12\u201321. Springer","DOI":"10.1007\/978-3-031-18409-3_2"},{"key":"21431_CR39","unstructured":"LeCun Y, Cortes C, Burges CJC (1998) The MNIST Database of Handwritten Digits. http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"21431_CR40","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Technical Report. University of Toronto"},{"key":"21431_CR41","unstructured":"Bhagoji AN, Chakraborty S, Mittal P, Calo S (2019) Analyzing Federated Learning through an Adversarial Lens. arxiv:1811.12470"},{"key":"21431_CR42","doi-asserted-by":"publisher","unstructured":"Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K (2017) Practical secure aggregation for privacy-preserving machine learning. ACM SIGSAC Conf Comput Commun Sec (CCS) 1175\u20131191. https:\/\/doi.org\/10.1145\/3133956.3133982","DOI":"10.1145\/3133956.3133982"},{"key":"21431_CR43","doi-asserted-by":"crossref","unstructured":"Qu L, Zhou Y, Liang PP, Xia Y, Wang F, Adeli E, Fei-Fei L, Rubin D (2022) Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning. arxiv:2106.06047","DOI":"10.1109\/CVPR52688.2022.00982"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21431-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21431-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21431-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:46:38Z","timestamp":1777707998000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21431-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,2]]},"references-count":44,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["21431"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21431-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,2]]},"assertion":[{"value":"28 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2026","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"495"}}