{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T08:40:02Z","timestamp":1748076002331,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819755615"},{"type":"electronic","value":"9789819755622"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5562-2_3","type":"book-chapter","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T07:01:50Z","timestamp":1729926110000},"page":"37-53","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Privacy Protection Bottom-up Hierarchical Federated Learning with Class Imbalanced Data"],"prefix":"10.1007","author":[{"given":"Jing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajia","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiufeng","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anzhen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,27]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Cheng, X., Shi, F., Liu, Y., Zhou, J., Liu, X., Huang, L.: A CLASS-imbalanced heterogeneous federated learning model for detecting icing on wind turbine blades. IEEE Transactions on Industrial Informatics (2022)","DOI":"10.1109\/TII.2022.3167467"},{"key":"3_CR2","unstructured":"Choudhury, O., Park, Y., Salonidis, T., Gkoulalas-Divanis, A., Sylla, I., et\u00a0al.: Predicting adverse drug reactions on distributed health data using federated learning. In: AMIA Annual symposium proceedings. vol.\u00a02019, p.\u00a0313. American Medical Informatics Association (2019)"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Duan, M., Liu, D., Chen, X., Liu, R., Tan, Y., Liang, L.: Self-balancing federated learning with global imbalanced data in mobile systems. IEEE Transactions on Parallel and Distributed Systems 32(1), 59\u201371 (2020)","DOI":"10.1109\/TPDS.2020.3009406"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Duan, M., Liu, D., Chen, X., Tan, Y., Ren, J., Qiao, L., Liang, L.: Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications. In: 2019 IEEE 37th international conference on computer design (ICCD). pp. 246\u2013254. IEEE (2019)","DOI":"10.1109\/ICCD46524.2019.00038"},{"key":"3_CR5","unstructured":"Green, M.C., Plumbley, M.D.: Federated learning with highly imbalanced audio data. arXiv preprint arXiv:2105.08550 (2021)"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Hauschild, A.C., Lemanczyk, M., Matschinske, J., Frisch, T., Zolotareva, O., Holzinger, A., Baumbach, J., Heider, D.: Federated random forests can improve local performance of predictive models for various healthcare applications. Bioinformatics 38(8), 2278\u20132286 (2022)","DOI":"10.1093\/bioinformatics\/btac065"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Hua, G., Zhu, L., Wu, J., Shen, C., Zhou, L., Lin, Q.: Blockchain-based federated learning for intelligent control in heavy haul railway. IEEE Access 8, 176830\u2013176839 (2020)","DOI":"10.1109\/ACCESS.2020.3021253"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Li, A., Cao, Y., Guo, J., Peng, H., Guo, Q., Yu, H.: Fedcss: Joint client-and-sample selection for hard sample-aware noise-robust federated learning. Proceedings of the ACM on Management of Data 1(3), 1\u201324 (2023)","DOI":"10.1145\/3617332"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Li, A., Zhang, L., Tan, J., Qin, Y., Wang, J., Li, X.Y.: Sample-level data selection for federated learning. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications. pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/INFOCOM42981.2021.9488723"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Li, Q., Diao, Y., Chen, Q., He, B.: Federated learning on non-iid data silos: An experimental study. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE). pp. 965\u2013978. IEEE (2022)","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"3_CR11","unstructured":"Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., He, B.: A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering (2021)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37(3), 50\u201360 (2020)","DOI":"10.1109\/MSP.2020.2975749"},{"key":"3_CR13","unstructured":"Li, X., Huang, K., Yang, W., Wang, S., Zhang, Z.: On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189 (2019)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Li, X.C., Zhan, D.C.: Fedrs: Federated learning with restricted softmax for label distribution non-iid data. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. pp. 995\u20131005 (2021)","DOI":"10.1145\/3447548.3467254"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, V., Fern\u00e1ndez, A., Garc\u00eda, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information sciences 250, 113\u2013141 (2013)","DOI":"10.1016\/j.ins.2013.07.007"},{"key":"3_CR16","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics. pp. 1273\u20131282. PMLR (2017)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Mrad, I., Samara, L., Abdellatif, A.A., Al-Abbasi, A., Hamila, R., Erbad, A.: Federated learning for uav swarms under class imbalance and power consumption constraints. arXiv preprint arXiv:2108.10748 (2021)","DOI":"10.1109\/GLOBECOM46510.2021.9685143"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Qi, T., Zhan, Y., Li, P., Guo, J., Xia, Y.: Hwamei: A learning-based synchronization scheme for hierarchical federated learning. In: 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS). pp. 534\u2013544. IEEE (2023)","DOI":"10.1109\/ICDCS57875.2023.00047"},{"key":"3_CR19","unstructured":"Shen, Z., Cervino, J., Hassani, H., Ribeiro, A.: An agnostic approach to federated learning with class imbalance. In: International Conference on Learning Representations (2021)"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., Zhou, Y.: A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM workshop on artificial intelligence and security. pp. 1\u201311 (2019)","DOI":"10.1145\/3338501.3357370"},{"key":"3_CR21","unstructured":"Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: Tackling the objective inconsistency problem in heterogeneous federated optimization. Advances in neural information processing systems 33, 7611\u20137623 (2020)"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Xiao, C., Wang, S.: An experimental study of class imbalance in federated learning. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). pp.\u00a01\u20137. IEEE (2021)","DOI":"10.1109\/SSCI50451.2021.9660072"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Yin, X., Zhu, Y., Hu, J.: A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions. ACM Computing Surveys (CSUR) 54(6), 1\u201336 (2021)","DOI":"10.1145\/3460427"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Yu, T., Li, T., Sun, Y., Nanda, S., Smith, V., Sekar, V., Seshan, S.: Learning context-aware policies from multiple smart homes via federated multi-task learning. In: 2020 IEEE\/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI). pp. 104\u2013115. IEEE (2020)","DOI":"10.1109\/IoTDI49375.2020.00017"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, D.Y., Kou, Z., Wang, D.: Fedsens: A federated learning approach for smart health sensing with class imbalance in resource constrained edge computing. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications. pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/INFOCOM42981.2021.9488776"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, Z., Chen, Q., Zheng, W., Leng, J., Guo, M.: Dubhe: Towards data unbiasedness with homomorphic encryption in federated learning client selection. In: 50th International Conference on Parallel Processing. pp. 1\u201310 (2021)","DOI":"10.1145\/3472456.3473513"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5562-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T07:59:52Z","timestamp":1748073592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5562-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755615","9789819755622"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5562-2_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gifu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}