{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T02:33:01Z","timestamp":1784601181619,"version":"3.55.0"},"publisher-location":"Cham","reference-count":40,"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_61","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"655-665","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SiFT: A Serial Framework with\u00a0Textual Guidance for\u00a0Federated Learning"],"prefix":"10.1007","author":[{"given":"Xuyang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weizhuo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Shi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruixuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"61_CR1","unstructured":"Acar, D.A.E., Zhao, Y., Matas, R., Mattina, M., Whatmough, P., Saligrama, V.: Federated learning based on dynamic regularization. In: ICLR (2020)"},{"key":"61_CR2","unstructured":"Achiam, J., et\u00a0al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"61_CR3","first-page":"700","volume":"27","author":"N Balachandar","year":"2020","unstructured":"Balachandar, N., Chang, K., Kalpathy-Cramer, J., Rubin, D.L.: Accounting for data variability in multi-institutional distributed deep learning for medical imaging. JAMIA 27, 700\u2013708 (2020)","journal-title":"JAMIA"},{"key":"61_CR4","unstructured":"Beltr\u00e1n, E.T.M., et al.: Decentralized federated learning: fundamentals, state of the art, frameworks, trends, and challenges. IEEE Commun. Surv. Tutorials (2023)"},{"key":"61_CR5","doi-asserted-by":"publisher","unstructured":"Castro, F.M., Mar\u00edn-Jim\u00e9nez, M.J., Guil, N., Schmid, C., Alahari, K.: End-to-end incremental learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 241\u2013257. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01258-8_15","DOI":"10.1007\/978-3-030-01258-8_15"},{"key":"61_CR6","first-page":"945","volume":"25","author":"K Chang","year":"2018","unstructured":"Chang, K., et al.: Distributed deep learning networks among institutions for medical imaging. JAMIA 25, 945\u2013954 (2018)","journal-title":"JAMIA"},{"key":"61_CR7","doi-asserted-by":"publisher","unstructured":"Chen, M., Jiang, M., Dou, Q., Wang, Z., Li, X.: FedSoup: improving generalization and personalization in federated learning via selective model interpolation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14221, pp. 318\u2013328. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43895-0_30","DOI":"10.1007\/978-3-031-43895-0_30"},{"key":"61_CR8","doi-asserted-by":"publisher","unstructured":"Deng, Z., et al.: FedGrav: an adaptive federated aggregation algorithm for multi-institutional medical image segmentation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14221, pp. 170\u2013180. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43895-0_16","DOI":"10.1007\/978-3-031-43895-0_16"},{"key":"61_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"61_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"61_CR11","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: Scaffold: stochastic controlled averaging for federated learning. In: ICML (2020)"},{"key":"61_CR12","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"61_CR13","doi-asserted-by":"crossref","unstructured":"Kumaran, D., Hassabis, D., McClelland, J.L.: What learning systems do intelligent agents need? Complementary learning systems theory updated. Trends Cogn. Sci. 20, 512\u2013534(2016)","DOI":"10.1016\/j.tics.2016.05.004"},{"key":"61_CR14","doi-asserted-by":"crossref","unstructured":"Li, Q., Diao, Y., Chen, Q., He, B.: Federated learning on NON-IID data silos: An experimental study. In: ICDE (2022)","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"61_CR15","doi-asserted-by":"crossref","unstructured":"Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"61_CR16","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. MLSys 2, 429\u2013450 (2020)"},{"key":"61_CR17","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. TPAMI 40, 2935\u20132947 (2017)","journal-title":"TPAMI"},{"key":"61_CR18","doi-asserted-by":"publisher","unstructured":"Liu, Q., Yang, H., Dou, Q., Heng, P.A.: Federated semi-supervised medical image classification via inter-client relation matching. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 325\u2013335. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_31","DOI":"10.1007\/978-3-030-87199-4_31"},{"key":"61_CR19","doi-asserted-by":"crossref","unstructured":"Liu, W., Chen, L., Zhang, W.: Decentralized federated learning: balancing communication and computing costs. IEEE T-SIPN 8, 131\u2013143 (2022)","DOI":"10.1109\/TSIPN.2022.3151242"},{"key":"61_CR20","unstructured":"McDonnell, M., Gong, D., Parvaneh, A., Abbasnejad, E., van\u00a0den Hengel, A.: RanPac: random projections and pre-trained models for continual learning. In: NeurIPS (2023)"},{"key":"61_CR21","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: AISTATS (2017)"},{"key":"61_CR22","doi-asserted-by":"crossref","unstructured":"Pfitzner, B., Steckhan, N., Arnrich, B.: Federated learning in a medical context: a systematic literature review. TOIT 21, 1\u201331 (2021)","DOI":"10.1145\/3412357"},{"key":"61_CR23","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: ICML (2021)"},{"key":"61_CR24","doi-asserted-by":"publisher","unstructured":"Roth, H.R., et al.: Federated whole prostate segmentation in MRI with personalized neural architectures. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 357\u2013366. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_34","DOI":"10.1007\/978-3-030-87199-4_34"},{"key":"61_CR25","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: MICCAI (2023)","DOI":"10.1007\/978-3-031-43895-0_39"},{"key":"61_CR26","unstructured":"Schmidt, W.F., Kraaijveld, M.A., Duin, R.P., et\u00a0al.: Feed forward neural networks with random weights. In: ICPR (1992)"},{"key":"61_CR27","unstructured":"Sun, Y., Shen, L., Huang, T., Ding, L., Tao, D.: FedSpeed: larger local interval, less communication round, and higher generalization accuracy. In: ICLR (2022)"},{"key":"61_CR28","first-page":"9587","volume":"34","author":"AZ Tan","year":"2022","unstructured":"Tan, A.Z., Yu, H., Cui, L., Yang, Q.: Towards personalized federated learning. TNNLS 34, 9587\u20139603 (2022)","journal-title":"TNNLS"},{"key":"61_CR29","doi-asserted-by":"crossref","unstructured":"Tarumi, S., Suzuki, M., Yoshida, H., Miyauchi, S., Kurazume, R.: Personalized federated learning for institutional prediction model using electronic health records: a covariate adjustment approach. In: EMBC (2023)","DOI":"10.1109\/EMBC40787.2023.10339940"},{"key":"61_CR30","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\u20139 (2018)","journal-title":"Sci. Data"},{"key":"61_CR31","doi-asserted-by":"publisher","unstructured":"Wang, F.Y., Zhou, D.W., Ye, H.J., Zhan, D.C.: FOSTER: feature boosting and compression for class-incremental learning. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13685, pp. 398\u2013414. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19806-9_23","DOI":"10.1007\/978-3-031-19806-9_23"},{"key":"61_CR32","doi-asserted-by":"publisher","unstructured":"Wang, M., et al.: Federated uncertainty-aware aggregation for fundus diabetic retinopathy staging. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14221, pp. 222\u2013232. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43895-0_21","DOI":"10.1007\/978-3-031-43895-0_21"},{"key":"61_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Z., Hu, Y., Yan, S., Wang, Z., Hou, R., Wu, C.: Efficient ring-topology decentralized federated learning with deep generative models for medical data in ehealthcare systems. Electronics 11, 1548 (2022)","DOI":"10.3390\/electronics11101548"},{"key":"61_CR34","doi-asserted-by":"publisher","unstructured":"Wu, Y., Zeng, D., Wang, Z., Shi, Y., Hu, J.: Federated contrastive learning for volumetric medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 367\u2013377. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_35","DOI":"10.1007\/978-3-030-87199-4_35"},{"key":"61_CR35","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","volume":"10","author":"J Yang","year":"2023","unstructured":"Yang, J., et al.: MedMNIST v2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification. Sci. Data 10, 41 (2023)","journal-title":"Sci. Data"},{"key":"61_CR36","doi-asserted-by":"publisher","unstructured":"Yang, Y., Cui, Z., Xu, J., Zhong, C., Zheng, W.S., Wang, R.: Continual learning with Bayesian model based on a fixed pre-trained feature extractor. Vis. Intell. 1, 5 (2023). https:\/\/doi.org\/10.1007\/s44267-023-00005-y","DOI":"10.1007\/s44267-023-00005-y"},{"key":"61_CR37","doi-asserted-by":"crossref","unstructured":"Yasunaga, M., Leskovec, J., Liang, P.: LinkBERT: pretraining language models with document links. arXiv preprint arXiv:2203.15827 (2022)","DOI":"10.18653\/v1\/2022.acl-long.551"},{"key":"61_CR38","doi-asserted-by":"publisher","unstructured":"Yuan, L., Liu, X., Yu, J., Li, Y.: A full-set tooth segmentation model based on improved PointNET++. Vis. Intell. 1, 21 (2023). https:\/\/doi.org\/10.1007\/s44267-023-00026-7","DOI":"10.1007\/s44267-023-00026-7"},{"key":"61_CR39","doi-asserted-by":"publisher","unstructured":"Zhang, W., Huang, Y., Zhang, T., Zou, Q., Zheng, W.S., Wang, R.: Adapter learning in pretrained feature extractor for continual learning of diseases. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14221, pp. 68\u201378. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43895-0_7","DOI":"10.1007\/978-3-031-43895-0_7"},{"key":"61_CR40","doi-asserted-by":"publisher","unstructured":"Zhou, Q., Zheng, G.: FedContrast-GPA: heterogeneous federated optimization via local contrastive learning and global process-aware aggregation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14221, pp. 660\u2013670. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43895-0_62","DOI":"10.1007\/978-3-031-43895-0_62"}],"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_61","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:21:00Z","timestamp":1727871660000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72117-5_61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721168","9783031721175"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72117-5_61","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"}}]}}