{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T04:58:52Z","timestamp":1775624332431,"version":"3.50.1"},"reference-count":169,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:00:00Z","timestamp":1668124800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:00:00Z","timestamp":1668124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["No. 2018YFC1604000"],"award-info":[{"award-number":["No. 2018YFC1604000"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61806138"],"award-info":[{"award-number":["No.61806138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.U1636220"],"award-info":[{"award-number":["No.U1636220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61961160707"],"award-info":[{"award-number":["No.61961160707"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61976212"],"award-info":[{"award-number":["No.61976212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Development Foundation of the Central Guiding Local","award":["No. YDZJSX2021A038"],"award-info":[{"award-number":["No. YDZJSX2021A038"]}]},{"name":"China University Industry-University-Research Collaborative Innovation Fund","award":["2021FNA04014"],"award-info":[{"award-number":["2021FNA04014"]}]},{"name":"Outstanding Innovation Project for Graduate Students of Taiyuan University of Science and Technology","award":["No.XCX211004"],"award-info":[{"award-number":["No.XCX211004"]}]},{"name":"Outstanding Innovation Project for Graduate Students of Taiyuan University of Science and Technology","award":["No.XCX212081"],"award-info":[{"award-number":["No.XCX212081"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s13042-022-01647-y","type":"journal-article","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T08:03:04Z","timestamp":1668153784000},"page":"513-535","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":681,"title":["A survey on federated learning: challenges and applications"],"prefix":"10.1007","volume":"14","author":[{"given":"Jie","family":"Wen","sequence":"first","affiliation":[]},{"given":"Zhixia","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Zhihua","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Jianghui","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Wensheng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"1647_CR1","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.ins.2021.11.027","volume":"583","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Zhao M et al (2022) An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty. Inf Sci 583:56\u201372","journal-title":"Inf Sci"},{"issue":"1","key":"1647_CR2","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1504\/IJBIC.2022.120756","volume":"19","author":"H Wang","year":"2022","unstructured":"Wang H, Xie F, Li J, Miu F (2022) Modelling, simulation and optimisation of medical enterprise warehousing process based on FlexSim model and greedy algorithm. Int J Bio-Inspired Comput 19(1):59\u201366","journal-title":"Int J Bio-Inspired Comput"},{"key":"1647_CR3","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.ins.2020.05.067","volume":"537","author":"X Cai","year":"2020","unstructured":"Cai X, Hu Z, Chen J (2020) A many-objective optimization recommendation algorithm based on knowledge mining. Inf Sci 537:148\u2013161","journal-title":"Inf Sci"},{"issue":"3","key":"1647_CR4","doi-asserted-by":"publisher","first-page":"273","DOI":"10.3390\/math7030273","volume":"7","author":"Y Ren","year":"2019","unstructured":"Ren Y, Sun Y et al (2019) Adaptive Makeup Transfer via Bat Algorithm. Mathematics 7(3):273","journal-title":"Mathematics"},{"key":"1647_CR5","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.ins.2022.03.027","volume":"596","author":"Y Yang","year":"2021","unstructured":"Yang Y, Cai J, Yang H, Zhao X (2021) Density clustering with divergence distance and automatic center selection. Inf Sci 596:414\u2013438","journal-title":"Inf Sci"},{"issue":"2","key":"1647_CR6","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1504\/IJBIC.2021.114089","volume":"17","author":"B Hemalatha","year":"2021","unstructured":"Hemalatha B, Rajkumar N (2021) A modified machine learning classification for dental age assessment with effectual ACM-JO based segmentation. Int J Bio-Inspired Comput 17(2):95\u2013104","journal-title":"Int J Bio-Inspired Comput"},{"key":"1647_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2021.07.077","volume":"579","author":"Z Cui","year":"2021","unstructured":"Cui Z, Zhao P et al (2021) An improved matrix factorization based model for many-objective optimization recommendation. Inf Sci 579:1\u201314","journal-title":"Inf Sci"},{"issue":"3","key":"1647_CR8","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1504\/IJBIC.2021.114877","volume":"17","author":"N Kuze","year":"2021","unstructured":"Kuze N, Ishikura S et al (2021) Classification of diversified web crawler accesses inspired by biological adaptation. Int J Bio-Inspired Comput 17(3):165\u2013173","journal-title":"Int J Bio-Inspired Comput"},{"key":"1647_CR9","unstructured":"Mcmahan H et al (2017) Communication-Efficient Learning of Deep Networks from Decentralized Data. In: proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54: 1273\u20131282"},{"issue":"2","key":"1647_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated Machine Learning: Concept and Applications. ACM Trans Intell Syst Technol 10(2):1\u201319","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"2","key":"1647_CR11","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1504\/IJBIC.2022.121236","volume":"19","author":"L Wang","year":"2022","unstructured":"Wang L, Meng Z, Yang L (2022) A multi-layer two-dimensional convolutional neural network for sentiment analysis. Int J Bio-Inspired Comput 19(2):97\u2013107","journal-title":"Int J Bio-Inspired Comput"},{"issue":"2","key":"1647_CR12","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1504\/IJCSM.2021.118797","volume":"14","author":"H Li","year":"2021","unstructured":"Li H (2021) Image error correction of hockey players\u2019 step-by-step pull shooting based on Bayesian classification. Int J Comput Sci Math 14(2):185\u2013195","journal-title":"Int J Comput Sci Math"},{"issue":"10","key":"1647_CR13","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1109\/TPDS.2021.3137321","volume":"33","author":"A Li","year":"2022","unstructured":"Li A, Zhang L, Wang J, Han F, Li X (2022) Privacy-Preserving Efficient Federated-Learning Model Debugging. IEEE Trans Parallel Distrib Syst 33(10):2291\u20132303","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"1","key":"1647_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/IJBIC.2022.120749","volume":"19","author":"A Pereira","year":"2022","unstructured":"Pereira A, Mazza L, Pinto P et al (2022) Deep convolutional neural network applied to Trypanosoma cruzi detection in blood samples. Int J Bio-Inspired Comput 19(1):1\u201317","journal-title":"Int J Bio-Inspired Comput"},{"issue":"4","key":"1647_CR15","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1504\/IJBIC.2021.119950","volume":"18","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Sai Y, Yan L (2021) An improved extension neural network methodology for fault diagnosis of complex electromechanical system. Int J Bio-Inspired Comput 18(4):250\u2013258","journal-title":"Int J Bio-Inspired Comput"},{"issue":"4","key":"1647_CR16","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1007\/s10115-022-01664-x","volume":"64","author":"J Liu","year":"2022","unstructured":"Liu J, Huang J, Zhou Y et al (2022) From distributed machine learning to federated learning: a survey. Knowl Inf Syst 64(4):885\u2013917","journal-title":"Knowl Inf Syst"},{"issue":"2","key":"1647_CR17","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MNET.011.2000331","volume":"35","author":"Z Cui","year":"2021","unstructured":"Cui Z, Zhao Y, Cao Y et al (2021) Malicious Code Detection under 5G HetNets Based on a Multi-Objective RBM Model. IEEE Network 35(2):82\u201387","journal-title":"IEEE Network"},{"key":"1647_CR18","doi-asserted-by":"publisher","first-page":"116410","DOI":"10.1016\/j.eswa.2021.116410","volume":"193","author":"B Liang","year":"2022","unstructured":"Liang B, Cai J, Yang H (2022) A new cell group clustering algorithm based on validation & correction mechanism. Expert Syst Appl 193:116410","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1647_CR19","doi-asserted-by":"publisher","first-page":"33","DOI":"10.23919\/CSMS.2021.0001","volume":"1","author":"T Long","year":"2021","unstructured":"Long T, Jia Q (2021) Matching Uncertain Renewable Supply with Electric Vehicle Charging Demand\u2014A Bi-Level Event-Based Optimization Method. Complex Syst Model Simul 1(1):33\u201344","journal-title":"Complex Syst Model Simul"},{"key":"1647_CR20","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1109\/TIFS.2022.3174394","volume":"17","author":"H Zhou","year":"2022","unstructured":"Zhou H, Yang G, Dai H, Liu G (2022) PFLF: Privacy-Preserving Federated Learning Framework for Edge Computing. IEEE Trans Inf Forensics Secur 17:1905\u20131918","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"3","key":"1647_CR21","doi-asserted-by":"publisher","first-page":"440","DOI":"10.3390\/electronics9030440","volume":"9","author":"J Jiang","year":"2020","unstructured":"Jiang J, Hu L et al (2020) BACombo-Bandwidth-Aware Decentralized Federated Learning. Electronics 9(3):440","journal-title":"Electronics"},{"issue":"4","key":"1647_CR22","doi-asserted-by":"publisher","first-page":"308","DOI":"10.23919\/CSMS.2021.0026","volume":"1","author":"C Wang","year":"2021","unstructured":"Wang C, Liu Z, Wei H, Chen L, Zhang H (2021) Hybrid Deep Learning Model for Short-Term Wind Speed Forecasting Based on Time Series Decomposition and Gated Recurrent Unit. Complex Syst Model Simul 1(4):308\u2013321","journal-title":"Complex Syst Model Simul"},{"key":"1647_CR23","doi-asserted-by":"publisher","first-page":"116963","DOI":"10.1016\/j.eswa.2022.116963","volume":"201","author":"Z Cui","year":"2022","unstructured":"Cui Z, Wen J, Lan Y et al (2022) Communication-efficient federated recommendation model based on many-objective evolutionary algorithm. Expert Syst Appl 201:116963","journal-title":"Expert Syst Appl"},{"issue":"5","key":"1647_CR24","doi-asserted-by":"publisher","first-page":"165817","DOI":"10.1007\/s11704-021-0598-z","volume":"16","author":"K Zhang","year":"2022","unstructured":"Zhang K, Song X, Zhang C, Yu C (2022) Challenges and future directions of secure federated learning: a survey. Front Comput Sci 16(5):165817","journal-title":"Front Comput Sci"},{"issue":"5","key":"1647_CR25","doi-asserted-by":"publisher","first-page":"3582","DOI":"10.1109\/TII.2021.3116132","volume":"18","author":"C Feng","year":"2022","unstructured":"Feng C, Liu B et al (2022) Blockchain-Empowered Decentralized Horizontal Federated Learning for 5G-Enabled UAVs. IEEE Trans Industr Inf 18(5):3582\u20133592","journal-title":"IEEE Trans Industr Inf"},{"key":"1647_CR26","doi-asserted-by":"publisher","first-page":"101465","DOI":"10.1016\/j.phycom.2021.101465","volume":"49","author":"M Dai","year":"2021","unstructured":"Dai M, Xu A, Huang Q, Zhang Z, Lin X (2021) Vertical federated DNN training. Phys Communication 49:101465","journal-title":"Phys Communication"},{"key":"1647_CR27","unstructured":"Gu B, Xu A et al (2020) Privacy-Preserving Asynchronous Vertical Federated Learning Algorithms for Multiparty Collaborative Learning. arXiv preprint arXiv: 2008. 06233"},{"issue":"2","key":"1647_CR28","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1504\/IJBIC.2021.118087","volume":"18","author":"B Li","year":"2021","unstructured":"Li B, Liang Y, Gan Z et al (2021) Research on multi-UAV task decision-making based on improved MADDPG algorithm and transfer learning. Int J Bio-Inspired Comput 18(2):82\u201391","journal-title":"Int J Bio-Inspired Comput"},{"issue":"11","key":"1647_CR29","doi-asserted-by":"publisher","first-page":"3351","DOI":"10.1007\/s13042-021-01415-4","volume":"12","author":"J Guan","year":"2021","unstructured":"Guan J, Cai J, Bai H, You I (2021) Deep transfer learning-based network traffic classification for scarce dataset in 5G IoT systems. Int J Mach Learn Cybernet 12(11):3351\u20133365","journal-title":"Int J Mach Learn Cybernet"},{"key":"1647_CR30","doi-asserted-by":"publisher","first-page":"112846","DOI":"10.1016\/j.eswa.2019.112846","volume":"139","author":"Y Yang","year":"2020","unstructured":"Yang Y, Cai J, Yang H, Zhang J, Zhao X (2020) TAD: A trajectory clustering algorithm based on spatial-temporal density analysis. Expert Syst Appl 139:112846","journal-title":"Expert Syst Appl"},{"issue":"6","key":"1647_CR31","doi-asserted-by":"publisher","first-page":"3293","DOI":"10.1007\/s10489-020-01887-x","volume":"51","author":"J Xu","year":"2021","unstructured":"Xu J, Zhang Z et al (2021) A many-objective optimized task allocation scheduling model in cloud computing. Appl Intell 51(6):3293\u20133310","journal-title":"Appl Intell"},{"issue":"11","key":"1647_CR32","doi-asserted-by":"publisher","first-page":"7650","DOI":"10.1109\/TII.2021.3051607","volume":"17","author":"X Cai","year":"2021","unstructured":"Cai X, Geng S, Zhang J et al (2021) A Sharding Scheme-Based Many-Objective Optimization Algorithm for Enhancing Security in Blockchain-Enabled Industrial Internet of Things. IEEE Trans Industr Inf 17(11):7650\u20137658","journal-title":"IEEE Trans Industr Inf"},{"issue":"1","key":"1647_CR33","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1504\/IJBIC.2021.113360","volume":"17","author":"U Cavusoglu","year":"2021","unstructured":"Cavusoglu U, Kokcam AH (2021) A new approach to design S-box generation algorithm based on genetic algorithm. Int J Bio-Inspired Comput 17(1):52\u201362","journal-title":"Int J Bio-Inspired Comput"},{"key":"1647_CR34","doi-asserted-by":"crossref","unstructured":"Yao A (1982) Protocols for secure computations. In: Proceedings of the 23rd Annual IEEE Symposium on Foundations of Computer Science pp.\u00a0160\u2013164","DOI":"10.1109\/SFCS.1982.38"},{"key":"1647_CR35","doi-asserted-by":"crossref","unstructured":"Bogdanov D, Willemson J (2008) Sharemind: A Framework for Fast Privacy-Preserving Computations. In: Proceedings of European Symposium on Research in Computer Security, Springer, pp.\u00a0192\u2013206","DOI":"10.1007\/978-3-540-88313-5_13"},{"issue":"1","key":"1647_CR36","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1109\/TCSVT.2021.3055072","volume":"32","author":"L Xiong","year":"2022","unstructured":"Xiong L, Han X, Yang C, Shi Y (2022) Robust Reversible Watermarking in Encrypted Image With Secure Multi-Party Based on Lightweight Cryptography. IEEE Trans Circuits Syst Video Technol 32(1):75\u201391","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"4","key":"1647_CR37","doi-asserted-by":"publisher","first-page":"4875","DOI":"10.1109\/TNSM.2021.3107718","volume":"18","author":"J An","year":"2021","unstructured":"An J, Wang Z et al (2021) Know Where You are: A Practical Privacy-Preserving Semi-Supervised Indoor Positioning via Edge-Crowdsensing. IEEE Trans Netw Serv Manage 18(4):4875\u20134887","journal-title":"IEEE Trans Netw Serv Manage"},{"key":"1647_CR38","doi-asserted-by":"publisher","unstructured":"Bonawitz K, Ivanov V, Kreuter B et al (2017) Practical Secure Aggregation for Privacy-Preserving Machine Learning. Presented at the Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, available: https:\/\/doi.org\/10.1145\/3133956.3133982","DOI":"10.1145\/3133956.3133982"},{"key":"1647_CR39","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.future.2021.10.017","volume":"128","author":"Y Xu","year":"2022","unstructured":"Xu Y, Peng C, Tan W et al (2022) Non-interactive verifiable privacy-preserving federated learning. Future Generation Computer Systems 128:365\u2013380","journal-title":"Future Generation Computer Systems"},{"key":"1647_CR40","unstructured":"Geyer R, Klein T, Nabi M (2017) Differentially Private Federated Learning: A Client Level Perspective. arXiv preprint arXiv: 1712. 07557"},{"key":"1647_CR41","doi-asserted-by":"crossref","unstructured":"Huang J, Cheng X, Ji Z et al (2022) AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X. Security and Communication Networks 2022: 9334943","DOI":"10.1155\/2022\/9334943"},{"issue":"2","key":"1647_CR42","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TII.2021.3073925","volume":"18","author":"Z Xiong","year":"2022","unstructured":"Xiong Z, Cai Z, Takabi D, Li W (2022) Privacy Threat and Defense for Federated Learning With Non-i.i.d. Data in AIoT. IEEE Trans Industr Inf 18(2):1310\u20131321","journal-title":"IEEE Trans Industr Inf"},{"issue":"9","key":"1647_CR43","doi-asserted-by":"publisher","first-page":"3400","DOI":"10.1109\/TNNLS.2019.2944481","volume":"31","author":"F Sattler","year":"2020","unstructured":"Sattler F, Wiedemann S et al (2020) Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data. IEEE Trans Neural Networks Learn Syst 31(9):3400\u20133413","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"2","key":"1647_CR44","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1504\/IJCSM.2021.114193","volume":"13","author":"M Ta\u015fk\u0131ran","year":"2021","unstructured":"Ta\u015fk\u0131ran M, Yeti\u015f S (2021) Deep learning based tobacco products classification. Int J Comput Sci Math 13(2):167\u2013176","journal-title":"Int J Comput Sci Math"},{"issue":"1","key":"1647_CR45","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.32604\/cmc.2022.022290","volume":"71","author":"Z Sun","year":"2022","unstructured":"Sun Z, Feng J, Yin L et al (2022) Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning. Cmc-Computers Mater Continua 71(1):1867\u20131886","journal-title":"Cmc-Computers Mater Continua"},{"issue":"20","key":"1647_CR46","doi-asserted-by":"publisher","first-page":"e6367","DOI":"10.1002\/cpe.6367","volume":"33","author":"T Fan","year":"2021","unstructured":"Fan T, Cui Z (2021) Adaptive differential privacy preserving based on multi-objective optimization in deep neural networks. Concurrency and Computation-Practice & Experience 33(20):e6367","journal-title":"Concurrency and Computation-Practice & Experience"},{"issue":"1","key":"1647_CR47","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1504\/IJBIC.2019.10022705","volume":"14","author":"X Cai","year":"2019","unstructured":"Cai X, Zhang M et al (2019) Analyses of inverted generational distance for many-objective optimisation algorithms. Int J Bio-Inspired Comput 14(1):62\u201368","journal-title":"Int J Bio-Inspired Comput"},{"issue":"1","key":"1647_CR48","doi-asserted-by":"publisher","first-page":"59","DOI":"10.23919\/CSMS.2022.0001","volume":"2","author":"W Li","year":"2022","unstructured":"Li W, Ye X, Huang Y, Mahmoodi S (2022) Adaptive Dimensional Learning with a Tolerance Framework for the Differential Evolution Algorithm. Complex Syst Model Simul 2(1):59\u201377","journal-title":"Complex Syst Model Simul"},{"issue":"4","key":"1647_CR49","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1504\/IJCSM.2021.120681","volume":"14","author":"J Xi","year":"2021","unstructured":"Xi J, Zheng L (2021) Cuckoo search with dual-subpopulation and information-sharing strategy. Int J Comput Sci Math 14(4):315\u2013327","journal-title":"Int J Comput Sci Math"},{"issue":"7","key":"1647_CR50","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1007\/s13042-019-01054-w","volume":"11","author":"W Wang","year":"2020","unstructured":"Wang W, Gan Y, Vong C, Chen C (2020) Homo-ELM: fully homomorphic extreme learning machine. Int J Mach Learn Cybernet 11(7):1531\u20131540","journal-title":"Int J Mach Learn Cybernet"},{"key":"1647_CR51","doi-asserted-by":"crossref","unstructured":"Zhang X, Fu A, Wang H et al (2020) A Privacy-Preserving and Verifiable Federated Learning Scheme. In: proceedings of the ICC 2020\u20132020 IEEE International Conference on Communications (ICC) pp.\u00a01\u20136","DOI":"10.1109\/ICC40277.2020.9148628"},{"key":"1647_CR52","unstructured":"Ma J, Naas S, Sigg S, Lyu X (2021) Privacy-preserving federated learning based on multi-key homomorphic encryption.arXiv preprint arXiv:2104. 06824"},{"issue":"2","key":"1647_CR53","doi-asserted-by":"publisher","first-page":"734","DOI":"10.3390\/app12020734","volume":"12","author":"J Park","year":"2022","unstructured":"Park J, Lim H (2022) Privacy-Preserving Federated Learning Using Homomorphic Encryption. Appl Sci 12(2):734","journal-title":"Appl Sci"},{"key":"1647_CR54","unstructured":"Zhang C, Li S, Xia J et al (2020) BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning. In: Proceedings of the 2020 USENIX Conference on Usenix Annual Technical Conference, USENIX Association, USA, pp.\u00a0493\u2013506"},{"issue":"5","key":"1647_CR55","doi-asserted-by":"publisher","first-page":"e5478","DOI":"10.1002\/cpe.5478","volume":"32","author":"X Cai","year":"2020","unstructured":"Cai X, Niu Y, Geng S et al (2020) An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search. Concurrency and Computation-Practice & Experience 32(5):e5478","journal-title":"Concurrency and Computation-Practice & Experience"},{"key":"1647_CR56","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.jpdc.2019.03.010","volume":"129","author":"Z Cui","year":"2019","unstructured":"Cui Z, Du L, Wang P et al (2019) Malicious code detection based on CNNs and multi-objective algorithm. J Parallel Distrib Comput 129:50\u201358","journal-title":"J Parallel Distrib Comput"},{"issue":"6","key":"1647_CR57","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1007\/s13042-016-0629-5","volume":"9","author":"P Chan","year":"2018","unstructured":"Chan P, He Z, Li H, Hsu C (2018) Data sanitization against adversarial label contamination based on data complexity. Int J Mach Learn Cybernet 9(6):1039\u20131052","journal-title":"Int J Mach Learn Cybernet"},{"key":"1647_CR58","doi-asserted-by":"publisher","first-page":"117018","DOI":"10.1016\/j.eswa.2022.117018","volume":"201","author":"Y Yang","year":"2022","unstructured":"Yang Y, Cai J, Yang H et al (2022) ISBFK-means: A new clustering algorithm based on influence space. Expert Syst Appl 201:117018","journal-title":"Expert Syst Appl"},{"key":"1647_CR59","doi-asserted-by":"crossref","unstructured":"Tian Y, Zhang W, Simpson A, Jiang Z (2021) Defending Against Data Poisoning Attacks: From Distributed Learning to Federated Learning.The Computer Journal,bxab192","DOI":"10.1093\/comjnl\/bxab192"},{"key":"1647_CR60","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.future.2020.12.003","volume":"117","author":"Y Qi","year":"2021","unstructured":"Qi Y, Hossain M, Nie J, Li X (2021) Privacy-preserving blockchain-based federated learning for traffic flow prediction. Future Generation Computer Systems-the International Journal of Escience 117:328\u2013337","journal-title":"Future Generation Computer Systems-the International Journal of Escience"},{"issue":"2","key":"1647_CR61","first-page":"241","volume":"13","author":"Z Cui","year":"2020","unstructured":"Cui Z, Xue F, Zhang S et al (2020) A Hybrid BlockChain-Based Identity Authentication Scheme for Multi-WSN. IEEE Trans Serv Comput 13(2):241\u2013251","journal-title":"IEEE Trans Serv Comput"},{"issue":"7","key":"1647_CR62","doi-asserted-by":"publisher","first-page":"e5906","DOI":"10.1002\/cpe.5906","volume":"34","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Chen J, Zhang J et al (2022) Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks. Concurrency and Computation: Practice and Experience 34(7):e5906","journal-title":"Concurrency and Computation: Practice and Experience"},{"issue":"1","key":"1647_CR63","doi-asserted-by":"publisher","first-page":"55","DOI":"10.23919\/CSMS.2021.0006","volume":"1","author":"X Li","year":"2021","unstructured":"Li X, Cao S, Gao L, Wen L et al (2021) A Threshold-Control Generative Adversarial Network Method for Intelligent Fault Diagnosis. Complex Syst Model Simul 1(1):55\u201364","journal-title":"Complex Syst Model Simul"},{"issue":"2","key":"1647_CR64","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1109\/JSAC.2021.3118347","volume":"40","author":"S Shi","year":"2022","unstructured":"Shi S, Hu C, Wang D, Zhu Y, Han Z (2022) Federated Anomaly Analytics for Local Model Poisoning Attack. IEEE J Sel Areas Commun 40(2):596\u2013610","journal-title":"IEEE J Sel Areas Commun"},{"issue":"2","key":"1647_CR65","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.3934\/mbe.2022078","volume":"19","author":"K Zhai","year":"2022","unstructured":"Zhai K, Ren Q, Wang L, Yan C (2022) Byzantine-robust federated learning via credibility assessment on non- IID data. Math Biosci Eng 19(2):1659\u20131676","journal-title":"Math Biosci Eng"},{"key":"1647_CR66","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3152156","author":"X Ma","year":"2022","unstructured":"Ma X, Jiang Q, Shojafar M et al (2022) DisBezant: Secure and Robust Federated Learning Against Byzantine Attack in IoT-Enabled MTS. IEEE Trans Intell Transp Syst. DOI: https:\/\/doi.org\/10.1109\/TITS.2022.3152156","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"2","key":"1647_CR67","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.3934\/mbe.2022078","volume":"19","author":"K Zhai","year":"2022","unstructured":"Zhai K, Ren Q, Wang J, Yan C (2022) Byzantine-robust federated learning via credibility assessment on non-IID data. Math Biosci Eng 19(2):1659\u20131676","journal-title":"Math Biosci Eng"},{"issue":"1","key":"1647_CR68","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1504\/IJCSM.2021.118078","volume":"14","author":"M Zhang","year":"2021","unstructured":"Zhang M, Mo L (2021) MGWHD-SVM: maximum weighted heteroscedastic migration learning algorithm. Int J Comput Sci Math 14(1):89\u2013106","journal-title":"Int J Comput Sci Math"},{"key":"1647_CR69","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06861-3","author":"W Li","year":"2022","unstructured":"Li W, Wang S (2022) Federated meta-learning for spatial-temporal prediction. Neural Comput Appl. DOI: https:\/\/doi.org\/10.1007\/s00521-021-06861-3","journal-title":"Neural Comput Appl"},{"key":"1647_CR70","unstructured":"McMahan H, Moore E, Ramage D, Arcas B (2016) Federated Learning of Deep Networks using Model Averaging.arXiv preprint arXiv:1602. 05629"},{"issue":"8","key":"1647_CR71","doi-asserted-by":"publisher","first-page":"1754","DOI":"10.1109\/TPDS.2020.2975189","volume":"31","author":"W Liu","year":"2020","unstructured":"Liu W, Chen L, Chen Y, Zhang W (2020) Accelerating Federated Learning via Momentum Gradient Descent. IEEE Trans Parallel Distrib Syst 31(8):1754\u20131766","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"1647_CR72","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.future.2021.09.015","volume":"127","author":"X Wu","year":"2022","unstructured":"Wu X, Zhang Y, Shi M et al (2022) An adaptive federated learning scheme with differential privacy preserving. Future Generation Computer Systems 127:362\u2013372","journal-title":"Future Generation Computer Systems"},{"issue":"4","key":"1647_CR73","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1109\/TCCN.2021.3084406","volume":"7","author":"H Wu","year":"2021","unstructured":"Wu H, Wang P (2021) Fast-Convergent Federated Learning With Adaptive Weighting. IEEE Trans Cogn Commun Netw 7(4):1078\u20131088","journal-title":"IEEE Trans Cogn Commun Netw"},{"issue":"6","key":"1647_CR74","doi-asserted-by":"publisher","first-page":"39","DOI":"10.23919\/JCC.2021.06.004","volume":"18","author":"W Bao","year":"2021","unstructured":"Bao W, Wu C et al (2021) Edge Computing-Based Joint Client Selection and Networking Scheme for Federated Learning in Vehicular IoT. China Commun 18(6):39\u201352","journal-title":"China Commun"},{"issue":"10","key":"1647_CR75","doi-asserted-by":"publisher","first-page":"2612","DOI":"10.1109\/TPDS.2022.3148113","volume":"33","author":"M Hu","year":"2020","unstructured":"Hu M, Wu D, Zhou Y et al (2020) Incentive-Aware Autonomous Client Participation in Federated Learning. IEEE Trans Parallel Distrib Syst 33(10):2612\u20132627","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"1","key":"1647_CR76","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1109\/TCOMM.2021.3124961","volume":"70","author":"S Liu","year":"2022","unstructured":"Liu S, Yu G, Yin R, Yuan J, Shen L, Liu C (2022) Joint Model Pruning and Device Selection for Communication-Efficient Federated Edge Learning. IEEE Trans Commun 70(1):231\u2013244","journal-title":"IEEE Trans Commun"},{"issue":"8","key":"1647_CR77","doi-asserted-by":"publisher","first-page":"1996","DOI":"10.1109\/TPDS.2021.3134647","volume":"33","author":"Y Deng","year":"2022","unstructured":"Deng Y, Lyu F, Ren J et al (2022) AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning. IEEE Trans Parallel Distrib Syst 33(8):1996\u20132009","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"1","key":"1647_CR78","doi-asserted-by":"publisher","first-page":"78","DOI":"10.23919\/CSMS.2022.0003","volume":"2","author":"Z Liao","year":"2021","unstructured":"Liao Z, Li S (2021) Solving Nonlinear Equations Systems with an Enhanced Reinforcement Learning Based Differential Evolution. Complex Syst Model Simul 2(1):78\u201395","journal-title":"Complex Syst Model Simul"},{"issue":"2","key":"1647_CR79","doi-asserted-by":"publisher","first-page":"131","DOI":"10.23919\/CSMS.2021.0013","volume":"1","author":"L Luo","year":"2021","unstructured":"Luo L, Zhao N, Lodewijks G (2021) Scheduling Storage Process of Shuttle-Based Storage and Retrieval Systems Based on Reinforcement Learning. Complex Syst Model Simul 1(2):131\u2013144","journal-title":"Complex Syst Model Simul"},{"key":"1647_CR80","unstructured":"Lai F, Zhu X, Madhyastha H, Chowdhury M (2021) Oort: Efficient federated learning via guided participant selection. In: Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2021, pp.\u00a019\u201335"},{"key":"1647_CR81","doi-asserted-by":"crossref","unstructured":"Nishio T, Yonetani R (2019) Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge. In: Proceedings of ICC 2019\u20132019 IEEE International Conference on Communications (ICC) pp.\u00a01\u20137","DOI":"10.1109\/ICC.2019.8761315"},{"issue":"2","key":"1647_CR82","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1049\/cmu2.12333","volume":"16","author":"C Du","year":"2022","unstructured":"Du C, Xiao J, Guo W (2022) Bandwidth constrained client selection and scheduling for federated learning over SD-WAN. IET Commun 16(2):187\u2013194","journal-title":"IET Commun"},{"issue":"1","key":"1647_CR83","doi-asserted-by":"publisher","first-page":"15","DOI":"10.23919\/CSMS.2021.0002","volume":"1","author":"W Gong","year":"2021","unstructured":"Gong W, Liao Z, Mi X et al (2021) Nonlinear Equations Solving with Intelligent Optimization Algorithms: A Survey. Complex Syst Model Simul 1(1):15\u201332","journal-title":"Complex Syst Model Simul"},{"issue":"2","key":"1647_CR84","doi-asserted-by":"publisher","first-page":"91","DOI":"10.23919\/CSMS.2021.0010","volume":"1","author":"F Zhao","year":"2021","unstructured":"Zhao F, Di S, Cao J et al (2021) A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems. Complex Syst Model Simul 1(2):91\u2013108","journal-title":"Complex Syst Model Simul"},{"issue":"2","key":"1647_CR85","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1504\/IJBIC.2021.114101","volume":"17","author":"J Li","year":"2021","unstructured":"Li J, Cao F, Cheng H, Qian Y (2021) Learning the number of filters in convolutional neural networks. Int J Bio-Inspired Comput 17(2):75\u201384","journal-title":"Int J Bio-Inspired Comput"},{"issue":"4","key":"1647_CR86","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1504\/IJBIC.2021.116615","volume":"17","author":"Y Hu","year":"2021","unstructured":"Hu Y, Yan X (2021) Neural network-assisted expensive optimisation algorithm for pollution source rapid positioning of drinking water. Int J Bio-Inspired Comput 17(4):227\u2013235","journal-title":"Int J Bio-Inspired Comput"},{"key":"1647_CR87","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2021.3119152","author":"K Li","year":"2021","unstructured":"Li K, Xiao C (2021) CBFL: A Communication-Efficient Federated Learning Framework From Data Redundancy Perspective. IEEE Syst J. DOI: https:\/\/doi.org\/10.1109\/JSYST.2021.3119152","journal-title":"IEEE Syst J"},{"issue":"12","key":"1647_CR88","first-page":"2571","volume":"57","author":"X Lu","year":"2020","unstructured":"Lu X, Liao Y, Lio P, Pan H (2020) An Asynchronous Federated Learning Mechanism for Edge Network Computing. J Comput Res Dev 57(12):2571\u20132582","journal-title":"J Comput Res Dev"},{"issue":"20","key":"1647_CR89","doi-asserted-by":"publisher","first-page":"15531","DOI":"10.1109\/JIOT.2021.3073112","volume":"8","author":"C Li","year":"2021","unstructured":"Li C, Li G, Varshney P (2021) Communication-Efficient Federated Learning Based on Compressed Sensing. IEEE Internet of Things Journal 8(20):15531\u201315541","journal-title":"IEEE Internet of Things Journal"},{"issue":"4","key":"1647_CR90","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1504\/IJCSM.2021.120688","volume":"14","author":"X Cheng","year":"2021","unstructured":"Cheng X, You M, Ma X (2021) Bi-level optimisation model of modular product family with adaptability consideration. Int J Comput Sci Math 14(4):357\u2013368","journal-title":"Int J Comput Sci Math"},{"key":"1647_CR91","doi-asserted-by":"publisher","DOI":"10.1145\/3522592","author":"J Cai","year":"2022","unstructured":"Cai J, Yang Y, Yang H, Zhao X, Hao J (2022) ACM Trans Knowl Discovery Data. DOI: https:\/\/doi.org\/10.1145\/3522592. ARIS: A Noise Insensitive Data Pre-processing Scheme for Data Reduction Using Influence Space"},{"issue":"1","key":"1647_CR92","doi-asserted-by":"publisher","first-page":"291","DOI":"10.23919\/CSMS.2021.0023","volume":"4","author":"Z Cui","year":"2021","unstructured":"Cui Z, Zhao L, Zeng Y et al (2021) A Novel PIO Algorithm with multiple selection strategies for many-objective optimization problems. Complex Syst Model Simul 4(1):291\u2013307","journal-title":"Complex Syst Model Simul"},{"issue":"21","key":"1647_CR93","doi-asserted-by":"publisher","first-page":"10003","DOI":"10.1109\/JSEN.2019.2927733","volume":"19","author":"X Cai","year":"2019","unstructured":"Cai X, Wang P et al (2019) Multi-Objective Three-Dimensional DV-Hop Localization Algorithm With NSGA-II. IEEE Sens J 19(21):10003\u201310015","journal-title":"IEEE Sens J"},{"issue":"1","key":"1647_CR94","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/IJCSM.2021.118073","volume":"14","author":"Z Hou","year":"2021","unstructured":"Hou Z, Hou J (2021) Joint estimation of battery state-of-charge based on the genetic algorithm - adaptive unscented Kalman filter. Int J Comput Sci Math 14(1):1\u201316","journal-title":"Int J Comput Sci Math"},{"issue":"2","key":"1647_CR95","doi-asserted-by":"publisher","first-page":"184","DOI":"10.3390\/math7020184","volume":"7","author":"P Wang","year":"2019","unstructured":"Wang P, Xue F, Li H et al (2019) A Multi-Objective DV-Hop Localization Algorithm Based on NSGA-II in Internet of Things. Mathematics 7(2):184","journal-title":"Mathematics"},{"issue":"1","key":"1647_CR96","doi-asserted-by":"publisher","first-page":"35","DOI":"10.23919\/CSMS.2022.0004","volume":"2","author":"K Qiao","year":"2022","unstructured":"Qiao K, Liang J, Qu B et al (2022) Differential Evolution with Level-Based Learning Mechanism. Complex Syst Model Simul 2(1):35\u201358","journal-title":"Complex Syst Model Simul"},{"issue":"4","key":"1647_CR97","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TNNLS.2019.2919699","volume":"31","author":"H Zhu","year":"2020","unstructured":"Zhu H, Jin Y (2020) Multi-Objective Evolutionary Federated Learning. IEEE Trans Neural Networks Learn Syst 31(4):1310\u20131322","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"1","key":"1647_CR98","first-page":"80","volume":"16","author":"Y Lan","year":"2022","unstructured":"Lan Y, Xie L, Cai X, Wang L (2022) A many-objective evolutionary algorithm based on integrated strategy for skin cancer detection. KSII Trans Internet Inf Syst 16(1):80\u201396","journal-title":"KSII Trans Internet Inf Syst"},{"issue":"10","key":"1647_CR99","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1007\/s00607-021-00970-6","volume":"103","author":"Q Wang","year":"2021","unstructured":"Wang Q, Li Q et al (2021) Efficient federated learning for fault diagnosis in industrial cloud-edge computing. Computing 103(10):2319\u20132337","journal-title":"Computing"},{"key":"1647_CR100","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-021-00989-x","author":"J Zhang","year":"2022","unstructured":"Zhang J, Chen X, Wang C et al (2022) FedAda: Fast-convergent adaptive federated learning in heterogeneous mobile edge computing environment. World Wide Web-Internet and Web Information Systems. DOI: https:\/\/doi.org\/10.1007\/s11280-021-00989-x","journal-title":"World Wide Web-Internet and Web Information Systems"},{"issue":"2","key":"1647_CR101","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TPDS.2020.3023905","volume":"32","author":"C Wang","year":"2021","unstructured":"Wang C, Yang Y, Zhou P (2021) Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity. IEEE Trans Parallel Distrib Syst 32(2):394\u2013410","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"1","key":"1647_CR102","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1109\/TCCN.2021.3100574","volume":"8","author":"A Ta\u00efk","year":"2022","unstructured":"Ta\u00efk A, Mlika Z, Cherkaoui S (2022) Data-Aware Device Scheduling for Federated Edge Learning. IEEE Trans Cogn Commun Netw 8(1):408\u2013421","journal-title":"IEEE Trans Cogn Commun Netw"},{"issue":"12","key":"1647_CR103","doi-asserted-by":"publisher","first-page":"3607","DOI":"10.1007\/s13042-021-01410-9","volume":"12","author":"K Hu","year":"2021","unstructured":"Hu K, Wu J, Weng L (2021) A novel federated learning approach based on the confidence of federated Kalman filters. Int J Mach Learn Cybernet 12(12):3607\u20133627","journal-title":"Int J Mach Learn Cybernet"},{"key":"1647_CR104","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699","author":"A Tan","year":"2022","unstructured":"Tan A, Yu H, Cui L, Yang Q (2022) Toward Personalized Federated Learning. IEEE Trans Neural Networks Learn Syst. DOI: https:\/\/doi.org\/10.1109\/TNNLS.2022.3160699","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"3","key":"1647_CR105","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TPDS.2021.3098467","volume":"33","author":"J Mills","year":"2022","unstructured":"Mills J, Hu J, Min G (2022) Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing. IEEE Trans Parallel Distrib Syst 33(3):630\u2013641","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"1647_CR106","doi-asserted-by":"publisher","first-page":"116310","DOI":"10.1016\/j.eswa.2021.116310","volume":"191","author":"X Ni","year":"2022","unstructured":"Ni X, Shen X, Zhao H (2022) Federated optimization via knowledge codistillation. Expert Syst Appl 191:116310","journal-title":"Expert Syst Appl"},{"issue":"2","key":"1647_CR107","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1109\/TNSE.2020.2996612","volume":"8","author":"H Yang","year":"2021","unstructured":"Yang H, He H, Zhang W, Cao X (2021) FedSteg: A Federated Transfer Learning Framework for Secure Image Steganalysis. IEEE Trans Netw Sci Eng 8(2):1084\u20131094","journal-title":"IEEE Trans Netw Sci Eng"},{"issue":"1","key":"1647_CR108","doi-asserted-by":"publisher","first-page":"953","DOI":"10.3934\/mbe.2022044","volume":"19","author":"S Liu","year":"2022","unstructured":"Liu S, Wang J, Zhang W (2022) Federated personalized random forest for human activity recognition. Math Biosci Eng 19(1):953\u2013971","journal-title":"Math Biosci Eng"},{"issue":"4","key":"1647_CR109","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1504\/IJCSM.2021.120689","volume":"14","author":"C Rao","year":"2021","unstructured":"Rao C, Li R (2021) Research on prediction method on RUL of motor of CNC machine based on deep learning. Int J Comput Sci Math 14(4):338\u2013346","journal-title":"Int J Comput Sci Math"},{"key":"1647_CR110","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03705-y","author":"B Liang","year":"2022","unstructured":"Liang B, Cai J, Yang H (2022) Grid-DPC: Improved density peaks clustering based on spatial grid walk. Appl Intell DOI. https:\/\/doi.org\/10.1007\/s10489-022-03705-y","journal-title":"Appl Intell DOI"},{"issue":"7","key":"1647_CR111","doi-asserted-by":"publisher","first-page":"4788","DOI":"10.1109\/TII.2021.3113708","volume":"18","author":"X Xu","year":"2022","unstructured":"Xu X, Peng H, Bhuiyan M et al (2022) Privacy-Preserving Federated Depression Detection From Multisource Mobile Health Data. IEEE Trans Industr Inf 18(7):4788\u20134797","journal-title":"IEEE Trans Industr Inf"},{"key":"1647_CR112","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.ins.2021.04.021","volume":"570","author":"L Ouyang","year":"2021","unstructured":"Ouyang L, Yuan Y, Cao Y, Wang F (2021) A novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts. Inf Sci 570:124\u2013143","journal-title":"Inf Sci"},{"issue":"10","key":"1647_CR113","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","volume":"27","author":"I Dayan","year":"2021","unstructured":"Dayan I, Poth H, Zhong A et al (2021) Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med 27(10):1735\u2013","journal-title":"Nat Med"},{"key":"1647_CR114","doi-asserted-by":"publisher","first-page":"860532","DOI":"10.3389\/fonc.2022.860532","volume":"12","author":"Z Ma","year":"2022","unstructured":"Ma Z, Zhang M, Liu J et al (2022) An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning. Front Oncol 12:860532","journal-title":"Front Oncol"},{"issue":"3","key":"1647_CR115","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1504\/IJBIC.2021.114881","volume":"17","author":"M Mabrouk","year":"2021","unstructured":"Mabrouk M, Afify H, Marzouk S (2021) 3D reconstruction of structural magnetic resonance neuroimaging based on computer aided detection. Int J Bio-Inspired Comput 17(3):174\u2013181","journal-title":"Int J Bio-Inspired Comput"},{"key":"1647_CR116","doi-asserted-by":"publisher","first-page":"113648","DOI":"10.1016\/j.eswa.2020.113648","volume":"159","author":"X Cai","year":"2020","unstructured":"Cai X, Hu Z, Zhao P et al (2020) A hybrid recommendation system with many-objective evolutionary algorithm. Expert Syst Appl 159:113648","journal-title":"Expert Syst Appl"},{"issue":"3","key":"1647_CR117","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1007\/s40747-021-00315-y","volume":"7","author":"L Xie","year":"2021","unstructured":"Xie L, Hu Z, Cai X et al (2021) Explainable recommendation based on knowledge graph and multi-objective optimization. Complex & Intelligent Systems 7(3):1241\u20131252","journal-title":"Complex & Intelligent Systems"},{"issue":"4","key":"1647_CR118","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1109\/TSC.2020.2964552","volume":"13","author":"Z Cui","year":"2020","unstructured":"Cui Z, Xu X, Xue F et al (2020) Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios. IEEE Trans Serv Comput 13(4):685\u2013695","journal-title":"IEEE Trans Serv Comput"},{"issue":"5","key":"1647_CR119","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MIS.2020.3017205","volume":"36","author":"G Lin","year":"2021","unstructured":"Lin G, Liang F, Pan W, Ming Z (2021) FedRec: Federated Recommendation With Explicit Feedback. IEEE Intell Syst 36(5):21\u201329","journal-title":"IEEE Intell Syst"},{"key":"1647_CR120","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-022-03709-z","author":"Z Jie","year":"2022","unstructured":"Jie Z, Chen S, Lai J, Arif M, He Z (2022) Personalized federated recommendation system with historical parameter clustering. J Ambient Intell Humaniz Comput. DOI: https:\/\/doi.org\/10.1007\/s12652-022-03709-z","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1647_CR121","doi-asserted-by":"publisher","first-page":"107700","DOI":"10.1016\/j.asoc.2021.107700","volume":"111","author":"Y Du","year":"2021","unstructured":"Du Y, Zhou D, Xie Y, Shi J, Gong M (2021) Federated matrix factorization for privacy-preserving recommender systems. Appl Soft Comput 111:107700","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1647_CR122","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/JIOT.2019.2944889","volume":"7","author":"S Duan","year":"2020","unstructured":"Duan S, Zhang D, Wang Y et al (2020) JointRec: A Deep-Learning-Based Joint Cloud Video Recommendation Framework for Mobile IoT. IEEE Internet of Things Journal 7(3):1655\u20131666","journal-title":"IEEE Internet of Things Journal"},{"issue":"1","key":"1647_CR123","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3233\/AIS-150360","volume":"8","author":"A Caballero","year":"2016","unstructured":"Caballero A, Garcia-Valverde T, Pereniguez F, Botia J (2016) Activity recommendation in intelligent campus environments based on the Eduroam federation. J Ambient Intell Smart Environ 8(1):35\u201346","journal-title":"J Ambient Intell Smart Environ"},{"key":"1647_CR124","doi-asserted-by":"crossref","unstructured":"Muhammad K, Wang Q, O\u2019Reilly-Morgan D et al (2020) FedFast: Going beyond Average for Faster Training of Federated Recommender Systems. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020, pp.\u00a01234\u20131242","DOI":"10.1145\/3394486.3403176"},{"issue":"2","key":"1647_CR125","doi-asserted-by":"publisher","first-page":"122","DOI":"10.23919\/CSMS.2021.0012","volume":"1","author":"F Wang","year":"2021","unstructured":"Wang F, Xu X, Chen M et al (2021) Simulation Research on Fire Evacuation of Large Public Buildings Based on Building Information Modeling. Complex Syst Model Simul 1(2):122\u2013130","journal-title":"Complex Syst Model Simul"},{"issue":"1","key":"1647_CR126","doi-asserted-by":"publisher","first-page":"18","DOI":"10.23919\/CSMS.2022.0005","volume":"2","author":"Y Shen","year":"2022","unstructured":"Shen Y, Yu L, Li J (2022) Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption. Complex Syst Model Simul 2(1):18\u201334","journal-title":"Complex Syst Model Simul"},{"key":"1647_CR127","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhu Z, Chang Y et al (2019) Demand Estimation of Water Resources based on Coupling Algorithm. In: Proceedings of the 31st Chinese Control and Decision Conference (2019 CCDC), pp.\u00a0714\u2013719","DOI":"10.1109\/CCDC.2019.8832522"},{"key":"1647_CR128","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.ins.2018.01.041","volume":"438","author":"H Wang","year":"2018","unstructured":"Wang H, Wang W, Cui Z et al (2018) A new dynamic firefly algorithm for demand estimation of water resources. Inf Sci 438:95\u2013106","journal-title":"Inf Sci"},{"issue":"1","key":"1647_CR129","doi-asserted-by":"publisher","first-page":"96","DOI":"10.23919\/CSMS.2021.0029","volume":"2","author":"H Lu","year":"2022","unstructured":"Lu H, Dong X, Cao X (2022) Motion Model of Floating Weather Sensing Node for Typhoon Detection. Complex Syst Model Simul 2(1):96\u2013111","journal-title":"Complex Syst Model Simul"},{"issue":"2","key":"1647_CR130","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1504\/IJCSM.2021.118796","volume":"14","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Xin D (2021) Short-term traffic flow prediction model based on deep learning regression algorithm. Int J Comput Sci Math 14(2):155\u2013166","journal-title":"Int J Comput Sci Math"},{"issue":"2","key":"1647_CR131","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1504\/IJBIC.2021.119201","volume":"18","author":"C Jiang","year":"2021","unstructured":"Jiang C, Li R, Chen J et al (2021) Modelling the green supply chain of hotels based on front-back stage decoupling: perspective of ant colony labour division. Int J Bio-Inspired Comput 18(2):176\u2013188","journal-title":"Int J Bio-Inspired Comput"},{"issue":"21","key":"1647_CR132","doi-asserted-by":"publisher","first-page":"6230","DOI":"10.3390\/s20216230","volume":"20","author":"J Jiang","year":"2020","unstructured":"Jiang J, Kantarci B, Oktug S, Soyata T (2020) Federated learning in smart city sensing: Challenges and opportunities. Sensors 20(21):6230","journal-title":"Sensors"},{"issue":"13","key":"1647_CR133","doi-asserted-by":"publisher","first-page":"4586","DOI":"10.3390\/s21134586","volume":"21","author":"K Putra","year":"2021","unstructured":"Putra K, Chen H, Prayitno (2021) Federated compressed learning edge computing framework with ensuring data privacy for pm2.5 prediction in smart city sensing applications. Sensors 21(13):4586","journal-title":"Sensors"},{"key":"1647_CR134","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3157056","author":"X Yuan","year":"2022","unstructured":"Yuan X, Chen J, Yang J et al (2022) FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction. IEEE Trans Intell Transp Syst. DOI: https:\/\/doi.org\/10.1109\/TITS.2022.3157056","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"3","key":"1647_CR135","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1504\/IJCSM.2021.119900","volume":"14","author":"L Liu","year":"2021","unstructured":"Liu L, Song M, Wang X et al (2021) Aircraft pushback slot allocation bi-level programming model based on congestion pricing. Int J Comput Sci Math 14(3):249\u2013262","journal-title":"Int J Comput Sci Math"},{"issue":"1","key":"1647_CR136","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/MNET.011.2000263","volume":"35","author":"Y Li","year":"2021","unstructured":"Li Y, Chen C, Liu N, Huang H et al (2021) A Blockchain-Based Decentralized Federated Learning Framework with Committee Consensus. IEEE Network 35(1):234\u2013241","journal-title":"IEEE Network"},{"issue":"6","key":"1647_CR137","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/MIS.2021.3082561","volume":"36","author":"K Cheng","year":"2021","unstructured":"Cheng K, Fan T, Jin Y et al (2021) SecureBoost: A Lossless Federated Learning Framework. IEEE Intell Syst 36(6):87\u201398","journal-title":"IEEE Intell Syst"},{"issue":"2","key":"1647_CR138","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1504\/IJBIC.2022.121225","volume":"19","author":"A Salawudeen","year":"2022","unstructured":"Salawudeen A, Umoh I, Sadiq B, Oyenike O, Mu\u2019azu M (2022) An adaptive ant colony optimisation for improved lane detection in intelligent automobile vehicles. Int J Bio-Inspired Comput 19(2):108\u2013123","journal-title":"Int J Bio-Inspired Comput"},{"issue":"1","key":"1647_CR139","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1504\/IJCSM.2022.122166","volume":"15","author":"Z Chen","year":"2022","unstructured":"Chen Z, Chen Z, Geng Y (2022) Modelling and empirical analysis of the VMI-3PL system of cloud service platform in industry supply chain. Int J Comput Sci Math 15(1):60\u201371","journal-title":"Int J Comput Sci Math"},{"key":"1647_CR140","doi-asserted-by":"publisher","first-page":"209191","DOI":"10.1109\/ACCESS.2020.3038287","volume":"8","author":"Y Ye","year":"2020","unstructured":"Ye Y, Li S, Liu F et al (2020) EdgeFed: Optimized Federated Learning Based on Edge Computing. IEEE Access 8:209191\u2013209198","journal-title":"IEEE Access"},{"key":"1647_CR141","doi-asserted-by":"publisher","first-page":"107569","DOI":"10.1016\/j.comnet.2020.107569","volume":"183","author":"H Jiang","year":"2020","unstructured":"Jiang H, Liu M, Yang B et al (2020) Customized Federated Learning for accelerated edge computing with heterogeneous task targets. Comput Netw 183:107569","journal-title":"Comput Netw"},{"issue":"10","key":"1647_CR142","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1007\/s00607-021-00970-6","volume":"103","author":"Q Wang","year":"2021","unstructured":"Wang Q, Li Q, Wang K et al (2021) Efficient federated learning for fault diagnosis in industrial cloud-edge computing. Computing 103(10):2319\u20132337","journal-title":"Computing"},{"issue":"6","key":"1647_CR143","doi-asserted-by":"publisher","first-page":"6073","DOI":"10.1109\/TVT.2021.3076780","volume":"70","author":"H Liu","year":"2021","unstructured":"Liu H, Zhang S, Zhang P et al (2021) Blockchain and Federated Learning for Collaborative Intrusion Detection in Vehicular Edge Computing. IEEE Trans Veh Technol 70(6):6073\u20136084","journal-title":"IEEE Trans Veh Technol"},{"key":"1647_CR144","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.comcom.2021.05.013","volume":"176","author":"N Chen","year":"2021","unstructured":"Chen N, Li Y, Liu X, Zhang Z (2021) A mutual information based federated learning framework for edge computing networks. Comput Commun 176:23\u201330","journal-title":"Comput Commun"},{"issue":"1","key":"1647_CR145","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1504\/IJCSM.2022.122160","volume":"15","author":"Z Zheng","year":"2022","unstructured":"Zheng Z, Wu S, Huang Q, Yang J (2022) Research on localisation algorithm of large irregular workpiece for industrial robot. Int J Comput Sci Math 15(1):30\u201342","journal-title":"Int J Comput Sci Math"},{"issue":"4","key":"1647_CR146","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1504\/IJBIC.2020.112353","volume":"16","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Cai X, Zhu H, Xu Y (2020) Application an improved swarming optimisation in attribute reduction. Int J Bio-Inspired Comput 16(4):213\u2013219","journal-title":"Int J Bio-Inspired Comput"},{"key":"1647_CR147","doi-asserted-by":"publisher","first-page":"100871","DOI":"10.1016\/j.swevo.2021.100871","volume":"63","author":"X Cai","year":"2021","unstructured":"Cai X, Geng S, Wu D, Chen J (2021) Unified integration of many-objective optimization algorithm based on temporary offspring for software defects prediction. Swarm Evol Comput 63:100871","journal-title":"Swarm Evol Comput"},{"issue":"22","key":"1647_CR148","doi-asserted-by":"publisher","first-page":"e5861","DOI":"10.1002\/cpe.5861","volume":"32","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Xie L (2020) A many-objective integrated evolutionary algorithm for feature selection in anomaly detection. Concurrency and Computation-Practice & Experience 32(22):e5861","journal-title":"Concurrency and Computation-Practice & Experience"},{"issue":"1","key":"1647_CR149","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s13042-021-01393-7","volume":"13","author":"M Melis","year":"2022","unstructured":"Melis M, Scalas M et al (2022) Do gradient-based explanations tell anything about adversarial robustness to android malware? Int J Mach Learn Cybernet 13(1):217\u2013232","journal-title":"Int J Mach Learn Cybernet"},{"issue":"10","key":"1647_CR150","doi-asserted-by":"publisher","first-page":"e6812","DOI":"10.1002\/cpe.6812","volume":"34","author":"Z Tang","year":"2022","unstructured":"Tang Z, Hu H, Xu C (2022) \u201cA federated learning method for network intrusion detection. Concurrency and Computation: Practice and Experience 34(10):e6812","journal-title":"Concurrency and Computation: Practice and Experience"},{"key":"1647_CR151","doi-asserted-by":"publisher","first-page":"101157","DOI":"10.1016\/j.phycom.2020.101157","volume":"42","author":"R Zhao","year":"2020","unstructured":"Zhao R, Yin Y, Shi Y, Xue Z (2020) Intelligent intrusion detection based on federated learning aided long short-term memory. Phys Communication 42:101157","journal-title":"Phys Communication"},{"issue":"8","key":"1647_CR152","doi-asserted-by":"publisher","first-page":"5615","DOI":"10.1109\/TII.2020.3023430","volume":"17","author":"B Li","year":"2021","unstructured":"Li B, Wu Y, Song J et al (2021) DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber-Physical Systems. IEEE Trans Industr Inf 17(8):5615\u20135624","journal-title":"IEEE Trans Industr Inf"},{"issue":"3","key":"1647_CR153","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1504\/IJBIC.2021.114876","volume":"17","author":"A Fallahpour","year":"2021","unstructured":"Fallahpour A, Barri K, Wong K et al (2021) An integrated data mining approach to predict electrical energy consumption. Int J Bio-Inspired Comput 17(3):142\u2013153","journal-title":"Int J Bio-Inspired Comput"},{"key":"1647_CR154","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.ins.2021.09.024","volume":"581","author":"X Cai","year":"2021","unstructured":"Cai X, Cao Y et al (2021) Multi-objective evolutionary 3D face reconstruction based on improved encoder-decoder network. Inf Sci 581:233\u2013248","journal-title":"Inf Sci"},{"issue":"6","key":"1647_CR155","doi-asserted-by":"publisher","first-page":"5234","DOI":"10.1109\/TVT.2021.3057074","volume":"70","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Cao Y, Cui Z et al (2021) A Many-Objective Optimization Based Intelligent Intrusion Detection Algorithm for Enhancing Security of Vehicular Networks in 6G. IEEE Trans Veh Technol 70(6):5234\u20135243","journal-title":"IEEE Trans Veh Technol"},{"issue":"11","key":"1647_CR156","doi-asserted-by":"publisher","first-page":"3145","DOI":"10.1007\/s13042-021-01306-8","volume":"12","author":"I Ko","year":"2021","unstructured":"Ko I, Chambers D, Barrett E (2021) Recurrent autonomous autoencoder for intelligent DDoS attack mitigation within the ISP domain. Int J Mach Learn Cybernet 12(11):3145\u20133167","journal-title":"Int J Mach Learn Cybernet"},{"issue":"1","key":"1647_CR157","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1504\/IJCSM.2022.122144","volume":"15","author":"O Al-Hazaimeh","year":"2022","unstructured":"Al-Hazaimeh O, Al-Jamal M, Alomari A et al (2022) Image encryption using anti-synchronisation and Bogdanov transformation map. Int J Comput Sci Math 15(1):43\u201359","journal-title":"Int J Comput Sci Math"},{"issue":"5","key":"1647_CR158","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1109\/MWC.011.2000501","volume":"28","author":"Z Qin","year":"2021","unstructured":"Qin Z, Li G, Ye H (2021) Federated Learning and Wireless Communications. IEEE Wirel Commun 28(5):134\u2013140","journal-title":"IEEE Wirel Commun"},{"issue":"2","key":"1647_CR159","doi-asserted-by":"publisher","first-page":"2193","DOI":"10.1109\/TVT.2021.3131852","volume":"71","author":"M Yang","year":"2022","unstructured":"Yang M, Qian H, Wang X, Zhou Y, Zhu H (2022) Client Selection for Federated Learning With Label Noise. IEEE Trans Veh Technol 71(2):2193\u20132197","journal-title":"IEEE Trans Veh Technol"},{"issue":"4","key":"1647_CR160","doi-asserted-by":"publisher","first-page":"257","DOI":"10.23919\/CSMS.2021.0027","volume":"1","author":"L Wang","year":"2021","unstructured":"Wang L, Pan Z, Wang J (2021) A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling. Complex Syst Model Simul 1(4):257\u2013270","journal-title":"Complex Syst Model Simul"},{"issue":"4","key":"1647_CR161","doi-asserted-by":"publisher","first-page":"335","DOI":"10.23919\/CSMS.2021.0024","volume":"1","author":"X Wu","year":"2021","unstructured":"Wu X, Cao Z, Wu S (2021) Real-Time Hybrid Flow Shop Scheduling Approach in Smart Manufacturing Environment. Complex Syst Model Simul 1(4):335\u2013350","journal-title":"Complex Syst Model Simul"},{"issue":"23","key":"1647_CR162","doi-asserted-by":"publisher","first-page":"18249","DOI":"10.1007\/s00500-020-05088-z","volume":"24","author":"X Cai","year":"2020","unstructured":"Cai X, Wang P et al (2020) Weight convergence analysis of DV-hop localization algorithm with GA. Soft Comput 24(23):18249\u201318258","journal-title":"Soft Comput"},{"issue":"4","key":"1647_CR163","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1504\/IJCSM.2021.117596","volume":"13","author":"W Peng","year":"2021","unstructured":"Peng W, Lin J, Ma X (2021) A bi-objective optimisation approach for the critical chain project scheduling problem. Int J Comput Sci Math 13(4):311\u2013330","journal-title":"Int J Comput Sci Math"},{"issue":"2","key":"1647_CR164","doi-asserted-by":"publisher","first-page":"130","DOI":"10.23919\/CSMS.2022.0006","volume":"2","author":"H Bai","year":"2022","unstructured":"Bai H, Fan T, Niu Y (2022) Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm. Complex Syst Model Simul 2(2):130\u2013141","journal-title":"Complex Syst Model Simul"},{"issue":"2","key":"1647_CR165","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1504\/IJCSM.2022.124002","volume":"15","author":"D Lv","year":"2022","unstructured":"Lv D (2022) Scale parameter recognition of blurred moving image based on edge combination algorithm. Int J Comput Sci Math 15(2):168\u2013182","journal-title":"Int J Comput Sci Math"},{"issue":"4","key":"1647_CR166","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1504\/IJCSM.2021.120686","volume":"14","author":"D Swain","year":"2021","unstructured":"Swain D, Bijawe S, Akolkar P et al (2021) Diabetic retinopathy using image processing and deep learning. Int J Comput Sci Math 14(4):397\u2013409","journal-title":"Int J Comput Sci Math"},{"issue":"12","key":"1647_CR167","doi-asserted-by":"publisher","first-page":"3665","DOI":"10.1109\/TFUZZ.2021.3089230","volume":"29","author":"X Cai","year":"2021","unstructured":"Cai X, Zhang J, Ning Z et al (2021) A Many-Objective Multistage Optimization-Based Fuzzy Decision-Making Model for Coal Production Prediction. IEEE Trans Fuzzy Syst 29(12):3665\u20133675","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1647_CR168","doi-asserted-by":"publisher","first-page":"8900","DOI":"10.1016\/j.egyr.2021.10.113","volume":"7","author":"S Chen","year":"2021","unstructured":"Chen S, Zhang J, Bai Y et al (2021) Blockchain Enabled Intelligence of Federated Systems (BELIEFS): An attack-tolerant trustable distributed intelligence paradigm. Energy Rep 7:8900\u20138911","journal-title":"Energy Rep"},{"key":"1647_CR169","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.ins.2020.01.018","volume":"518","author":"Z Cui","year":"2020","unstructured":"Cui Z, Zhang J et al (2020) Hybrid many-objective particle swarm optimization algorithm for green coal production problem. Inf Sci 518:256\u2013271","journal-title":"Inf Sci"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01647-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01647-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01647-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:19:18Z","timestamp":1744157958000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01647-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,11]]},"references-count":169,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["1647"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01647-y","relation":{"references":[{"id-type":"doi","id":"10.1145\/3522592","asserted-by":"subject"}]},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,11]]},"assertion":[{"value":"16 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}