{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:48:07Z","timestamp":1777128487156,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819608133","type":"print"},{"value":"9789819608140","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-0814-0_19","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T17:30:00Z","timestamp":1734024600000},"page":"285-300","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Communication-Concerned Federated Learning Framework Based on\u00a0Clustering Selection"],"prefix":"10.1007","author":[{"given":"Weifeng","family":"Sun","sequence":"first","affiliation":[]},{"given":"Ailian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zunjing","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Yipeng","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Abubakar, M.M., Umar, A.Z., Abubakar, M.: Personal data and privacy protection regulations: state of compliance with Nigeria data protection regulations (NDPR) in ministries, departments, and agencies (MDAS). In: 2022 5th Information Technology for Education and Development (ITED), pp.\u00a01\u20136 (2022)","DOI":"10.1109\/ITED56637.2022.10051182"},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Chai, Z., et al.: TiFL: a tier-based federated learning system. In: Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing, pp. 125\u2013136 (2020)","DOI":"10.1145\/3369583.3392686"},{"key":"19_CR3","unstructured":"Cho, Y.J., Wang, J., Joshi, G.: Client selection in federated learning: convergence analysis and power-of-choice selection strategies. CoRR. arXiv:2010.01243 (2020)"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Escudero-Vi\u00f1olo, M., L\u00f3pez-Cifuentes, A.: CCL: class-wise curriculum learning for class imbalance problems. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 1476\u20131480 (2022)","DOI":"10.1109\/ICIP46576.2022.9897273"},{"key":"19_CR5","unstructured":"Fraboni, Y., Vidal, R., Kameni, L., Lorenzi, M.: Clustered sampling: low-variance and improved representativity for clients selection in federated learning. 139, 3407\u20133416 (2021)"},{"key":"19_CR6","unstructured":"Lai, F., Zhu, X., Madhyastha, H.V., Chowdhury, M.: Oort: informed participant selection for scalable federated learning. CoRR. arXiv:2010.06081 (2020)"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Li, Q., Diao, Y., Chen, Q., He, B.: Federated learning on non-IID data silos: an experimental study. arXiv:2102.02079 (2021)","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"19_CR8","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks (2020)"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Ning, B.: Federated learning optimisation algorithm based on non-IID data. In: 2023 IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE), pp. 1663\u20131669 (2023)","DOI":"10.1109\/ICEACE60673.2023.10442280"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Ribero, M., Vikalo, H.: Communication-efficient federated learning via optimal client sampling. CoRR. arXiv:2007.15197 (2020)","DOI":"10.52591\/lxai2020071310"},{"key":"19_CR11","unstructured":"Sarkar, D., Narang, A., Rai, S.: Fed-focal loss for imbalanced data classification in federated learning. CoRR. arXiv:2011.06283 (2020)"},{"key":"19_CR12","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7\u20139, 2015, Conference Track Proceedings. arXiv:1409.1556 (2015)"},{"key":"19_CR13","doi-asserted-by":"publisher","first-page":"63713","DOI":"10.1109\/ACCESS.2024.3396155","volume":"12","author":"L Tao","year":"2024","unstructured":"Tao, L., Li, H., Wang, F., Liu, M., Tang, Z., Wang, Q.: An adaptive safe-region diversity oversampling algorithm for imbalanced classification. IEEE Access 12, 63713\u201363724 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3396155","journal-title":"IEEE Access"},{"key":"19_CR14","unstructured":"Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: Tackling the objective inconsistency problem in heterogeneous federated optimization. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6\u201312, 2020, Virtual (2020)"},{"key":"19_CR15","unstructured":"Xie, C., Koyejo, S., Gupta, I.: Asynchronous federated optimization. CoRR. arXiv:1903.03934 (2019)"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Xu, X., Duan, S., Zhang, J., Luo, Y., Zhang, D.: Optimizing federated learning on device heterogeneity with A sampling strategy. In: 29th IEEE\/ACM International Symposium on Quality of Service, IWQOS 2021, Tokyo, Japan, June 25\u201328, 2021, pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/IWQOS52092.2021.9521361"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Yao, X., Huang, C., Sun, L.: Two-stream federated learning: reduce the communication costs. In: IEEE Visual Communications and Image Processing, VCIP 2018, Taichung, Taiwan, December 9\u201312, 2018, pp.\u00a01\u20134. IEEE (2018)","DOI":"10.1109\/VCIP.2018.8698609"},{"key":"19_CR18","unstructured":"Yoon, T., Shin, S., Hwang, S.J., Yang, E.: FedMix: approximation of mixup under mean augmented federated learning. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3\u20137, 2021. OpenReview.net (2021)"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Yoshida, N., Nishio, T., Morikura, M., Yamamoto, K., Yonetani, R.: Hybrid-FL: cooperative learning mechanism using non-IID data in wireless networks. CoRR, pp. 1\u20137. arXiv:1905.07210 (2019)","DOI":"10.1109\/ICC40277.2020.9149323"},{"issue":"1","key":"19_CR20","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1443","volume":"12","author":"B Yu","year":"2022","unstructured":"Yu, B., Mao, W., Lv, Y., Zhang, C., Xie, Y.: A survey on federated learning in data mining. Wiley Interdiscip. Rev. Data Mining Knowl. Discov. 12(1), e1443 (2022)","journal-title":"Wiley Interdiscip. Rev. Data Mining Knowl. Discov."},{"issue":"1","key":"19_CR21","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1109\/TPDS.2021.3090331","volume":"33","author":"Y Zhou","year":"2022","unstructured":"Zhou, Y., Ye, Q., Lv, J.: Communication-efficient federated learning with compensated overlap-FedAvg. IEEE Trans. Parallel Distrib. Syst. 33(1), 192\u2013205 (2022)","journal-title":"IEEE Trans. Parallel Distrib. Syst."}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0814-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T18:06:35Z","timestamp":1734026795000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0814-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"ISBN":["9789819608133","9789819608140"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0814-0_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}