{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T10:05:50Z","timestamp":1779617150467,"version":"3.53.1"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698837","type":"print"},{"value":"9789819698844","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-981-96-9884-4_11","type":"book-chapter","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T12:25:07Z","timestamp":1753446307000},"page":"127-137","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fairness-Aware Federated Learning Based on Feature Attention and Contribution Calibration"],"prefix":"10.1007","author":[{"given":"Wenzheng","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanjing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyong","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Den","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingfeng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronghui","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, C., Xie, Y., Bai, H., et al.: A survey on federated learning. Knowl.-Based Syst. 216, 106775 (2021)","DOI":"10.1016\/j.knosys.2021.106775"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., et al.: Federated learning: challenges, methods, and future directions. IEEE Sig. Process. Mag. 37(3), 50\u201360 (2020)","DOI":"10.1109\/MSP.2020.2975749"},{"key":"11_CR3","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"11_CR4","unstructured":"Zhao, Y., Li, M., Lai, L., et al.: Federated learning with non-IID data. arXiv preprint arXiv:1806.00582 (2018)"},{"key":"11_CR5","unstructured":"Agarwal, A., Beygelzimer, A., Dud\u00edk, M., et al.: A reductions approach to fair classification. In: Proceedings of the International Conference on Machine Learning, pp. 60\u201369 (2018)"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Verma, S., Rubin, J.: Fairness definitions explained. In: Proceedings of the International Workshop on Software Fairness, pp. 1\u20137 (2018)","DOI":"10.1145\/3194770.3194776"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Ramchoun, H., Ghanou, Y., Ettaouil, M., et al.: multilayer perceptron: architecture optimization and training. Int. J. Interact. Multimedia Artif. Intell. (2016)","DOI":"10.9781\/ijimai.2016.415"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Badar, M., Sikdar, S., Nejdl, W., et al.: Fairtrade: achieving Pareto-optimal trade-offs between balanced accuracy and fairness in federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 10, pp. 10962\u201310970 (2024)","DOI":"10.1609\/aaai.v38i10.28971"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Nagalapatti, L., Narayanam, R.: Game of gradients: mitigating irrelevant clients in federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 10, pp. 9046\u20139054 (2021)","DOI":"10.1609\/aaai.v35i10.17093"},{"key":"11_CR11","unstructured":"Bache, K., Lichman, M.: UCI Machine Learning Repository. Irvine, CA, USA (2013)"},{"key":"11_CR12","unstructured":"Wightman, L.F.: LSAC National Longitudinal Bar Passage Study. LSAC Research Report Series (1998)"},{"key":"11_CR13","unstructured":"McMahan, B., Moore, E., Ramage, D., et al.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 1273\u20131282 (2017)"},{"key":"11_CR14","unstructured":"Zeng, Y., Chen, H., Lee, K.: Improving fairness via federated learning. arXiv preprint arXiv:2110.15545 (2021)"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Ezzeldin, Y.H., Yan, S., He, C.,et al.: Fairfed: enabling group fairness in federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no, 6, pp. 7494\u20137502 (2023)","DOI":"10.1609\/aaai.v37i6.25911"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Badar, M., Nejdl, W., Fisichella, M.: FAC-fed: federated adaptation for fairness and concept drift aware stream classification. Mach. Learn. 112(8), 2761\u20132786 (2023)","DOI":"10.1007\/s10994-023-06360-7"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9884-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T09:46:56Z","timestamp":1779616016000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9884-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698837","9789819698844"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9884-4_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}