{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:59:40Z","timestamp":1775667580520,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721168","type":"print"},{"value":"9783031721175","type":"electronic"}],"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\/978-3-031-72117-5_25","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"263-272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Enhancing Federated Learning Performance Fairness via\u00a0Collaboration Graph-Based Reinforcement Learning"],"prefix":"10.1007","author":[{"given":"Yuexuan","family":"Xia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benteng","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"issue":"3","key":"25_CR1","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1001\/jamaophthalmol.2013.1743","volume":"131","author":"MD Abr\u00e0moff","year":"2013","unstructured":"Abr\u00e0moff, M.D., et al.: Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 131(3), 351\u2013357 (2013)","journal-title":"JAMA Ophthalmol."},{"key":"25_CR2","unstructured":"Bao, W., Wang, H., Wu, J., He, J.: Optimizing the collaboration structure in cross-silo federated learning. In: ICML, vol.\u00a0202, pp. 1718\u20131736 (2023)"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Chen, F., Long, G., Wu, Z., Zhou, T., Jiang, J.: Personalized federated learning with a graph. In: IJCAI, pp. 2575\u20132582 (2022)","DOI":"10.24963\/ijcai.2022\/357"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Chen, J., Ma, B., Cui, H., Xia, Y.: Think twice before selection: federated evidential active learning for medical image analysis with domain shifts. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.01087"},{"issue":"2","key":"25_CR5","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.irbm.2013.01.010","volume":"34","author":"E Decenciere","year":"2013","unstructured":"Decenciere, E., et al.: TeleOphta: machine learning and image processing methods for teleophthalmology. Irbm 34(2), 196\u2013203 (2013)","journal-title":"Irbm"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Hosseini, S.M., Sikaroudi, M., Babaie, M., Tizhoosh, H.: Proportionally fair hospital collaborations in federated learning of histopathology images. IEEE TMI (2023)","DOI":"10.1109\/TMI.2023.3234450"},{"issue":"4","key":"25_CR7","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.1109\/TNSE.2022.3169117","volume":"9","author":"Z Hu","year":"2022","unstructured":"Hu, Z., Shaloudegi, K., Zhang, G., Yu, Y.: Federated learning meets multi-objective optimization. IEEE Trans. Netw. Sci. Eng. 9(4), 2039\u20132051 (2022)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, M., et al.: Fair federated medical image segmentation via client contribution estimation. In: CVPR, pp. 16302\u201316311 (2023)","DOI":"10.1109\/CVPR52729.2023.01564"},{"issue":"12","key":"25_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000083","volume":"14","author":"P Kairouz","year":"2021","unstructured":"Kairouz, P., McMahan, H.B., Avent, B., et al.: Advances and open problems in federated learning. Found. Trends Mach. Learn. 14(12), 1\u2013210 (2021)","journal-title":"Found. Trends Mach. Learn."},{"key":"25_CR10","unstructured":"Karthick, M., Sohier, D.: Aptos 2019 blindness detection. Kaggle https:\/\/kaggle.com\/competitions\/aptos2019-blindness-detection Go to reference in chapter (2019)"},{"key":"25_CR11","unstructured":"Kinga, D., Adam, J.B., et\u00a0al.: A method for stochastic optimization. In: ICLR, vol.\u00a05, p.\u00a06 (2015)"},{"key":"25_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"key":"25_CR13","unstructured":"Li, T., Beirami, A., Sanjabi, M., Smith, V.: Tilted empirical risk minimization. In: ICLR (2021)"},{"issue":"3","key":"25_CR14","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37(3), 50\u201360 (2020)","journal-title":"IEEE Signal Process. Mag."},{"key":"25_CR15","unstructured":"Li, T., Sanjabi, M., Beirami, A., Smith, V.: Fair resource allocation in federated learning. In: ICLR (2020)"},{"key":"25_CR16","unstructured":"Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. In: ICLR (2016)"},{"issue":"9","key":"25_CR17","first-page":"2713","volume":"39","author":"Q Liu","year":"2020","unstructured":"Liu, Q., Dou, Q., Yu, L., Heng, P.A.: MS-Net: multi-site network for improving prostate segmentation with heterogeneous MRI data. IEEE TMI 39(9), 2713\u20132724 (2020)","journal-title":"IEEE TMI"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Liu, R., et\u00a0al.: DeepDRiD: diabetic retinopathy grading and image quality estimation challenge. Patterns 3(6) (2022)","DOI":"10.1016\/j.patter.2022.100512"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Ma, B., Zhang, J., Xia, Y., Tao, D.: VNAS: variational neural architecture search. Int. J. Comput. Vis. 1\u201325 (2024)","DOI":"10.1007\/s11263-024-02014-w"},{"key":"25_CR20","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: AISTATS (2017)"},{"key":"25_CR21","unstructured":"Mohri, M., Sivek, G., Suresh, A.T.: Agnostic federated learning. In: ICML (2019)"},{"issue":"3","key":"25_CR22","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3390\/data3030025","volume":"3","author":"P Porwal","year":"2018","unstructured":"Porwal, P., et al.: Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data 3(3), 25 (2018)","journal-title":"Data"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Roth, H.R., et\u00a0al.: Federated learning for breast density classification: a real-world implementation. In: DART at MICCAI (2020)","DOI":"10.1007\/978-3-030-60548-3_18"},{"key":"25_CR24","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: ICML, pp. 6105\u20136114 (2019)"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Tang, X., Yu, H.: Competitive-cooperative multi-agent reinforcement learning for auction-based federated learning. In: IJCAI (2023)","DOI":"10.24963\/ijcai.2023\/474"},{"issue":"1","key":"25_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1\u20139 (2018)","journal-title":"Sci. Data"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Wang, Z., Fan, X., Qi, J., Wen, C., Wang, C., Yu, R.: Federated learning with fair averaging. In: IJCAI, pp. 1615\u20131623 (2021)","DOI":"10.24963\/ijcai.2021\/223"},{"key":"25_CR28","unstructured":"Zhang, G., Malekmohammadi, S., Chen, X., Yu, Y.: Proportional fairness in federated learning. TMLR 2023 (2023)"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, S.Q., Lin, J., Zhang, Q.: A multi-agent reinforcement learning approach for efficient client selection in federated learning. In: AAAI (2022)","DOI":"10.1609\/aaai.v36i8.20894"},{"issue":"3","key":"25_CR30","first-page":"818","volume":"40","author":"Y Zhou","year":"2020","unstructured":"Zhou, Y., Wang, B., Huang, L., Cui, S., Shao, L.: A benchmark for studying diabetic retinopathy: segmentation, grading, and transferability. IEEE TMI 40(3), 818\u2013828 (2020)","journal-title":"IEEE TMI"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72117-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:15:26Z","timestamp":1727871326000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72117-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721168","9783031721175"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72117-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","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 article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}