{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,13]],"date-time":"2025-04-13T02:38:18Z","timestamp":1744511898972,"version":"3.37.3"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013076","name":"National Major Science and Technology Projects of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013076","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.knosys.2024.112201","type":"journal-article","created":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T09:51:53Z","timestamp":1720950713000},"page":"112201","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["FedGA: A greedy approach to enhance federated learning with Non-IID data"],"prefix":"10.1016","volume":"301","author":[{"given":"Yue","family":"Cong","sequence":"first","affiliation":[]},{"given":"Yuxiang","family":"Zeng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4202-7802","authenticated-orcid":false,"given":"Jing","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Zhongyang","family":"Fang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7431","authenticated-orcid":false,"given":"Lejun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Du","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9409-5359","authenticated-orcid":false,"given":"Zhihong","family":"Tian","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2024.112201_b1","article-title":"Intelligent machinery fault diagnosis with event-based camera","author":"Li","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"3","key":"10.1016\/j.knosys.2024.112201_b2","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1109\/JAS.2023.124107","article-title":"Dynamic vision enabled contactless cross-domain machine fault diagnosis with neuromorphic computing","volume":"11","author":"Chen","year":"2024","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"10.1016\/j.knosys.2024.112201_b3","doi-asserted-by":"crossref","unstructured":"J. Lu, H. Zuo, G. Zhang, Fuzzy multiple-source transfer learning, IEEE Trans. Fuzzy Syst. 28 (12) 3418\u20133431.","DOI":"10.1109\/TFUZZ.2019.2952792"},{"key":"10.1016\/j.knosys.2024.112201_b4","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"year":"2018","series-title":"Federated learning with non-IID data","author":"Zhao","key":"10.1016\/j.knosys.2024.112201_b5"},{"issue":"3","key":"10.1016\/j.knosys.2024.112201_b6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3625558","article-title":"Heterogeneous federated learning: State-of-the-art and research challenges","volume":"56","author":"Ye","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.knosys.2024.112201_b7","article-title":"Model aggregation techniques in federated learning: A comprehensive survey","author":"Qi","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.knosys.2024.112201_b8","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proc. Mach. Learning Syst."},{"key":"10.1016\/j.knosys.2024.112201_b9","series-title":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","first-page":"1227","article-title":"FedDANE: A federated Newton-type method","author":"Li","year":"2019"},{"year":"2020","series-title":"Federated learning with matched averaging","author":"Wang","key":"10.1016\/j.knosys.2024.112201_b10"},{"year":"2021","series-title":"FedBN: Federated learning on non-IID features via local batch normalization","author":"Li","key":"10.1016\/j.knosys.2024.112201_b11"},{"key":"10.1016\/j.knosys.2024.112201_b12","series-title":"2019 IEEE 37th International Conference on Computer Design","first-page":"246","article-title":"Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications","author":"Duan","year":"2019"},{"key":"10.1016\/j.knosys.2024.112201_b13","series-title":"IEEE INFOCOM 2020-IEEE Conference on Computer Communications","first-page":"1698","article-title":"Optimizing federated learning on non-IID data with reinforcement learning","author":"Wang","year":"2020"},{"key":"10.1016\/j.knosys.2024.112201_b14","series-title":"ICC 2019-2019 IEEE International Conference on Communications","first-page":"1","article-title":"Client selection for federated learning with heterogeneous resources in mobile edge","author":"Nishio","year":"2019"},{"year":"2016","series-title":"Federated learning: Strategies for improving communication efficiency","author":"Konen","key":"10.1016\/j.knosys.2024.112201_b15"},{"key":"10.1016\/j.knosys.2024.112201_b16","series-title":"Federated learning of deep networks using model averaging","first-page":"2","author":"McMahan","year":"2016"},{"key":"10.1016\/j.knosys.2024.112201_b17","doi-asserted-by":"crossref","unstructured":"Mart\u00ed, N. Abadi, A. Chu, et al., Deep learning with differential privacy, in: 23rd ACM Conference on Computer and Communications Security, ACM CCS, 2016.","DOI":"10.1145\/2976749.2978318"},{"key":"10.1016\/j.knosys.2024.112201_b18","series-title":"International Symposium on Cyber Security Cryptography and Machine Learning","first-page":"361","article-title":"Turning HATE into LOVE: homomorphic ad hoc threshold encryption for scalable MPC","author":"Reyzin","year":"2021"},{"issue":"2","key":"10.1016\/j.knosys.2024.112201_b19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298981","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.knosys.2024.112201_b20","doi-asserted-by":"crossref","unstructured":"J. Lu, A. Liu, Y. Song, G. Zhang, Data-driven decision support under concept drift in streamed big data, Complex Intell. Syst. 6 (1) 157\u2013163.","DOI":"10.1007\/s40747-019-00124-4"},{"key":"10.1016\/j.knosys.2024.112201_b21","doi-asserted-by":"crossref","unstructured":"H. Chen, A. Frikha, D. Krompass, et al., FRAug: Tackling federated learning with Non-IID features via representation augmentation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 4849\u20134859.","DOI":"10.1109\/ICCV51070.2023.00447"},{"issue":"1","key":"10.1016\/j.knosys.2024.112201_b22","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s11280-022-01046-x","article-title":"Multi-center federated learning: clients clustering for better personalization","volume":"26","author":"Long","year":"2023","journal-title":"World Wide Web"},{"key":"10.1016\/j.knosys.2024.112201_b23","series-title":"International Conference on Machine Learning","first-page":"39879","article-title":"Feddisco: Federated learning with discrepancy-aware collaboration","author":"Ye","year":"2023"},{"issue":"9","key":"10.1016\/j.knosys.2024.112201_b24","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2024.112201_b25","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.knosys.2024.112201_b26","series-title":"Proceedings of the 2005 International Conference on Advances in Intelligent Computing","first-page":"878","article-title":"Borderline-SMOTE:a new over-sampling method in imbalanced data sets learning","author":"Hui","year":"2005"},{"key":"10.1016\/j.knosys.2024.112201_b27","series-title":"Proceeding of the 2009 Pacific-a-Sia Conference on Knowledge Discovery and Data Mining","first-page":"475","article-title":"Safe-level-smote:safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem","author":"Bunkhumpornpat","year":"2009"},{"key":"10.1016\/j.knosys.2024.112201_b28","series-title":"Proc of the 39th Int ACM SIGIR Conf on Research and Development in Information Retrieval","first-page":"805","article-title":"Distributional random oversampling for imbalanced text classification","author":"Moreo","year":"2016"},{"key":"10.1016\/j.knosys.2024.112201_b29","first-page":"2672","article-title":"Generative adversarial nets","volume":"3","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"year":"2016","series-title":"Deep Learning","author":"Goodfellow","key":"10.1016\/j.knosys.2024.112201_b30"},{"key":"10.1016\/j.knosys.2024.112201_b31","series-title":"Communication, Control, Computing","article-title":"Characterization and computation of local Nash equilibria in continuous games","author":"Ratli","year":"2006"},{"key":"10.1016\/j.knosys.2024.112201_b32","series-title":"International Conference on Machine Learning","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"Arjovsky","year":"2017"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705124008359?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705124008359?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T00:30:27Z","timestamp":1725582627000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705124008359"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":32,"alternative-id":["S0950705124008359"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2024.112201","relation":{},"ISSN":["0950-7051"],"issn-type":[{"type":"print","value":"0950-7051"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FedGA: A greedy approach to enhance federated learning with Non-IID data","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2024.112201","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112201"}}