{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T09:39:38Z","timestamp":1743068378980,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031731181"},{"type":"electronic","value":"9783031731198"}],"license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"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\/978-3-031-73119-8_6","type":"book-chapter","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T13:02:24Z","timestamp":1728478944000},"page":"53-62","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data Heterogeneity-Aware Personalized Federated Learning for Diagnosis"],"prefix":"10.1007","author":[{"given":"Huiyan","family":"Lin","sequence":"first","affiliation":[]},{"given":"Heng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Haojin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiangyang","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Kuai","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Chenhao","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Jiang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"unstructured":"Arivazhagan, M.G., Aggarwal, V., Singh, A.K., Choudhary, S.: Federated learning with personalization layers. arXiv preprint arXiv:1912.00818 (2019)","key":"6_CR1"},{"unstructured":"Collins, L., Hassani, H., Mokhtari, A., Shakkottai, S.: Exploiting shared representations for personalized federated learning. In: International conference on machine learning(ICML). pp. 2089\u20132099 (2021)","key":"6_CR2"},{"doi-asserted-by":"crossref","unstructured":"Huang, Y., Chu, L., Zhou, Z., Wang, L., Liu, J., Pei, J., Zhang, Y.: Personalized cross-silo federated learning on non-iid data. In: Proceedings of the AAAI conference on artificial intelligence (AAAI). pp. 7865\u20137873 (2021)","key":"6_CR3","DOI":"10.1609\/aaai.v35i9.16960"},{"doi-asserted-by":"crossref","unstructured":"Kulkarni, V., Kulkarni, M., Pant, A.: Survey of personalization techniques for federated learning. In: World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). pp. 794\u2013797 (2020)","key":"6_CR4","DOI":"10.1109\/WorldS450073.2020.9210355"},{"doi-asserted-by":"crossref","unstructured":"Li, H., Li, H., Chen, J., Qiu, Z., Fu, H., Wang, L., Hu, Y., Liu, J.: Raffesdg: random frequency filtering enabled single-source domain generalization for medical image segmentation. arXiv preprint (2024)","key":"6_CR5","DOI":"10.1007\/978-3-031-43987-2_13"},{"doi-asserted-by":"crossref","unstructured":"Li, H., Lin, Z., Qiu, Z., Li, Z., Niu, K., Guo, N., Fu, H., Hu, Y., Liu, J.: Enhancing and adapting in the clinic: source-free unsupervised domain adaptation for medical image enhancement. Transactions on Medical Imaging(TMI) (2023)","key":"6_CR6","DOI":"10.1109\/TMI.2023.3335651"},{"unstructured":"Liang, P.P., Liu, T., Ziyin, L., Allen, N.B., Auerbach, R.P., Brent, D., Salakhutdinov, R., Morency, L.P.: Think locally, act globally: federated learning with local and global representations. arXiv preprint arXiv:2001.01523 (2020)","key":"6_CR7"},{"doi-asserted-by":"crossref","unstructured":"Luo, J., Wu, S.: Adapt to adaptation: learning personalization for cross-silo federated learning. In: International Joint Conferences on Artificial Intelligence (IJCAI). p.\u00a02166 (2022)","key":"6_CR8","DOI":"10.24963\/ijcai.2022\/301"},{"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 (AISTATS). pp. 1273\u20131282 (2017)","key":"6_CR9"},{"doi-asserted-by":"crossref","unstructured":"Ren, K., Zou, K., Liu, X., Chen, Y., Yuan, X., Shen, X., Wang, M., Fu, H.: Uncertainty-informed mutual learning for joint medical image classification and segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). pp. 35\u201345 (2023)","key":"6_CR10","DOI":"10.1007\/978-3-031-43901-8_4"},{"doi-asserted-by":"crossref","unstructured":"Saha, P., Mishra, D., Noble, J.A.: Rethinking semi-supervised federated learning: How to co-train fully-labeled and fully-unlabeled client imaging data. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). pp. 414\u2013424 (2023)","key":"6_CR11","DOI":"10.1007\/978-3-031-43895-0_39"},{"doi-asserted-by":"crossref","unstructured":"Tan, Y., Long, G., Liu, L., Zhou, T., Lu, Q., Jiang, J., Zhang, C.: Fedproto: Federated prototype learning across heterogeneous clients. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). pp. 8432\u20138440 (2022)","key":"6_CR12","DOI":"10.1609\/aaai.v36i8.20819"},{"unstructured":"Tan, Y., Long, G., Ma, J., Liu, L., Zhou, T., Jiang, J.: Federated learning from pre-trained models: A contrastive learning approach. Advances in Neural Information Processing Systems(NeurIPS) (2022)","key":"6_CR13"},{"doi-asserted-by":"crossref","unstructured":"Wang, M., Lin, T., Wang, L., Lin, A., Zou, K., Xu, X., Zhou, Y., Peng, Y., Meng, Q., Qian, Y., et\u00a0al.: Uncertainty-inspired open set learning for retinal anomaly identification. Nature Communications (2023)","key":"6_CR14","DOI":"10.1038\/s41467-023-42444-7"},{"doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F., Lyu, L., Huang, Y., Xie, X.: Communication-efficient federated learning via knowledge distillation. Nature communications (2022)","key":"6_CR15","DOI":"10.1038\/s41467-022-29763-x"},{"unstructured":"Xu, J., Tong, X., Huang, S.L.: Personalized federated learning with feature alignment and classifier collaboration. In: International Conference on Learning Representations (ICLR) (2023)","key":"6_CR16"},{"doi-asserted-by":"crossref","unstructured":"Yang, J., Shi, R., Ni, B.: Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis. In: International Symposium on Biomedical Imaging (ISBI). pp. 191\u2013195 (2021)","key":"6_CR17","DOI":"10.1109\/ISBI48211.2021.9434062"},{"doi-asserted-by":"crossref","unstructured":"Yi, L., Wang, G., Liu, X., Shi, Z., Yu, H.: FedGH: Heterogeneous Federated Learning with Generalized Global Header. In: ACM Multimedia (2023)","key":"6_CR18","DOI":"10.1145\/3581783.3611781"},{"doi-asserted-by":"crossref","unstructured":"Zhang, J., Hua, Y., Wang, H., Song, T., Xue, Z., Ma, R., Cao, J., Guan, H.: Gpfl: Simultaneously learning global and personalized feature information for personalized federated learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). pp. 5041\u20135051 (2023)","key":"6_CR19","DOI":"10.1109\/ICCV51070.2023.00465"},{"doi-asserted-by":"crossref","unstructured":"Zhang, J., Hua, Y., Wang, H., Song, T., Xue, Z., Ma, R., Guan, H.: Fedala: Adaptive local aggregation for personalized federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). pp. 11237\u201311244 (2023)","key":"6_CR20","DOI":"10.1609\/aaai.v37i9.26330"},{"unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018)","key":"6_CR21"},{"doi-asserted-by":"crossref","unstructured":"Zou, K., Chen, Z., Yuan, X., Shen, X., Wang, M., Fu, H.: A review of uncertainty estimation and its application in medical imaging. arXiv preprint arXiv:2302.08119 (2023)","key":"6_CR22","DOI":"10.1016\/j.metrad.2023.100003"}],"container-title":["Lecture Notes in Computer Science","Ophthalmic Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73119-8_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T13:03:28Z","timestamp":1728479008000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73119-8_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"ISBN":["9783031731181","9783031731198"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73119-8_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"10 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Ophthalmic Medical Image Analysis","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":"10 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"omia2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/workshops.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}