{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:36:09Z","timestamp":1778168169788,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,13]]},"DOI":"10.1145\/3706890.3706891","type":"proceedings-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T13:37:20Z","timestamp":1736775440000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Federated Learning in non-IID Brain Tumor Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9088-6672","authenticated-orcid":false,"given":"Chengqi","family":"Gong","sequence":"first","affiliation":[{"name":"City University of Macau, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4238-3295","authenticated-orcid":false,"given":"Ximeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Fuzhou University, Fuzhou, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1930-9654","authenticated-orcid":false,"given":"Junjie","family":"Zhou","sequence":"additional","affiliation":[{"name":"City University of Macau, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"e_1_3_3_1_1_2","volume-title":"Federated learning based on dynamic regularization. arXiv preprint","author":"Acar D. A. E.","year":"2021","unstructured":"Acar, D. A. E., Zhao, Y., Navarro, R. M., Mattina, M., Whatmough, P. N., & Saligrama, V. Federated learning based on dynamic regularization. arXiv preprint 2021, arXiv:2111.04263."},{"key":"e_1_3_3_1_2_2","volume-title":"Federated learning based on dynamic regularization. arXiv preprint","author":"Acar D. A. E.","year":"2021","unstructured":"Acar, D. A. E., Zhao, Y., Navarro, R. M., Mattina, M., Whatmough, P. N., & Saligrama, V. Federated learning based on dynamic regularization. arXiv preprint 2021, arXiv:2111.04263."},{"key":"e_1_3_3_1_3_2","volume-title":"Federated learning and differential privacy for medical image analysis.\u00a0Scientific reports","author":"Adnan M.","year":"2022","unstructured":"Adnan, M., Kalra, S., Cresswell, J. C., Taylor, G. W., & Tizhoosh, H. R. Federated learning and differential privacy for medical image analysis.\u00a0Scientific reports, 2022, 12(1), 1953."},{"key":"e_1_3_3_1_4_2","volume-title":"Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation.\" PloS one","author":"Cheng","year":"2016","unstructured":"Cheng, Jun, et al. \"Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation.\" PloS one 2016, 11.6. Matlab source codes are available on github https:\/\/github.com\/chengjun583\/brainTumorRetrieval"},{"key":"e_1_3_3_1_5_2","first-page":"3","volume-title":"In\u00a0International MICCAI Brainlesion Workshop","author":"Chowdhury A.","year":"2021","unstructured":"Chowdhury, A., Kassem, H., Padoy, N., Umeton, R., & Karargyris, A. A review of medical federated learning: Applications in oncology and cancer research. In\u00a0International MICCAI Brainlesion Workshop, Cham: Springer International Publishing. 2021, September. pp. 3-24."},{"key":"e_1_3_3_1_6_2","first-page":"10112","volume-title":"In\u00a0Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"Gao L.","year":"2022","unstructured":"Gao, L., Fu, H., Li, L., Chen, Y., Xu, M., & Xu, C. Z. Feddc: Federated learning with non-iid data via partial drift decoupling and correction. In\u00a0Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 2022, (pp. 10112-10121)."},{"key":"e_1_3_3_1_7_2","first-page":"448","volume-title":"In\u00a0International conference on machine learning, pmlr.","author":"Ioffe S.","year":"2015","unstructured":"Ioffe, S., & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In\u00a0International conference on machine learning, pmlr. 2015, June. pp. 448-456."},{"key":"e_1_3_3_1_8_2","first-page":"1087","volume-title":"In\u00a0Proceedings of the AAAI Conference on Artificial Intelligence","author":"Jiang M.","year":"2022","unstructured":"Jiang, M., Wang, Z., & Dou, Q. Harmofl: Harmonizing partial and global drifts in federated learning on heterogeneous medical images. In\u00a0Proceedings of the AAAI Conference on Artificial Intelligence, 2022, June. (Vol. 36, No. 1, pp. 1087-1095)."},{"key":"e_1_3_3_1_9_2","volume-title":"In\u00a0International conference on machine learning, PMLR.","author":"Karimireddy S. P.","year":"2020","unstructured":"Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S., & Suresh, A. T. Scaffold: Stochastic controlled averaging for federated learning. In\u00a0International conference on machine learning, PMLR. 2020, November. (pp. 5132-5143)."},{"key":"e_1_3_3_1_10_2","first-page":"455","volume-title":"In\u00a0International MICCAI Brainlesion Workshop","author":"Khan M. I.","year":"2021","unstructured":"Khan, M. I., Jafaritadi, M., Alhoniemi, E., Kontio, E., & Khan, S. A. Adaptive weight aggregation in federated learning for brain tumor segmentation. In\u00a0International MICCAI Brainlesion Workshop, Cham: Springer International Publishing. 2021, September. (pp. 455-469)."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"e_1_3_3_1_12_2","volume-title":"Federated optimization in heterogeneous networks.\u00a0Proceedings of Machine learning and systems","author":"Li T.","year":"2020","unstructured":"Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. Federated optimization in heterogeneous networks.\u00a0Proceedings of Machine learning and systems, 2020, 2, 429-450."},{"key":"e_1_3_3_1_13_2","volume-title":"Fedbn: Federated learning on non-iid features via partial batch normalization. arXiv preprint","author":"Li X.","year":"2021","unstructured":"Li, X., Jiang, M., Zhang, X., Kamp, M., & Dou, Q. Fedbn: Federated learning on non-iid features via partial batch normalization. arXiv preprint 2021, arXiv:2102.07623."},{"key":"e_1_3_3_1_14_2","first-page":"1231","volume-title":"Neuro-oncology","author":"Louis D. N.","year":"2021","unstructured":"Louis, D. N., Perry, A., Wesseling, P., Brat, D. J., Cree, I. A., Figarella-Branger, D., ... & Ellison, D. W. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-oncology, 2021, 23(8), 1231-1251."},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.05.003"},{"key":"e_1_3_3_1_16_2","volume-title":"Optimize weight sharing for aggregation model in federated learning environment of brain tumor classification.\u00a0Journal of Al-Qadisiyah for computer science and mathematics","author":"Mahlool D. H.","year":"2022","unstructured":"Mahlool, D. H., & Abed, M. H. Optimize weight sharing for aggregation model in federated learning environment of brain tumor classification.\u00a0Journal of Al-Qadisiyah for computer science and mathematics, 2022, 14(3), Page-76."},{"key":"e_1_3_3_1_17_2","first-page":"1273","volume-title":"PMLR.","author":"McMahan B.","year":"2017","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. In\u00a0Artificial intelligence and statistics, PMLR. 2017, April. (pp. 1273-1282)."},{"key":"e_1_3_3_1_18_2","volume-title":"Brain and other central nervous system tumor statistics","author":"Miller K. D.","year":"2021","unstructured":"Miller, K. D., Ostrom, Q. T., Kruchko, C., Patil, N., Tihan, T., Cioffi, G., ... & Barnholtz\u2010Sloan, J. S. Brain and other central nervous system tumor statistics, 2021. CA: a cancer journal for clinicians, 2021, 71(5), 381-406."},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12020275"},{"key":"e_1_3_3_1_20_2","first-page":"444","volume-title":"In\u00a0International MICCAI Brainlesion Workshop","author":"Nalawade S.","year":"2021","unstructured":"Nalawade, S., Ganesh, C., Wagner, B., Reddy, D., Das, Y., Yu, F. F., ... & Maldjian, J. A. Federated learning for brain tumor segmentation using mri and transformers. In\u00a0International MICCAI Brainlesion Workshop, Cham: Springer International Publishing. 2021, September. (pp. 444-454)."},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00982"},{"key":"e_1_3_3_1_22_2","author":"Rauniyar A.","year":"2023","unstructured":"Rauniyar, A., Hagos, D. H., Jha, D., H\u00e5keg\u00e5rd, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions.\u00a0IEEE Internet of Things Journal. 2023.","journal-title":"Journal."},{"key":"e_1_3_3_1_23_2","volume-title":"S. Robust Learning Protocol for Federated Tumor Segmentation Challenge. In\u00a0International MICCAI Brainlesion Workshop. Cham: Springer Nature Switzerland.","author":"Rawat A.","year":"2022","unstructured":"Rawat, A., Zizzo, G., Kadhe, S., Epperlein, J. P., & Braghin, S. Robust Learning Protocol for Federated Tumor Segmentation Challenge. In\u00a0International MICCAI Brainlesion Workshop. Cham: Springer Nature Switzerland. 2022, September. (pp. 183-195)."},{"key":"e_1_3_3_1_24_2","volume-title":"How does batch normalization help optimization?\u00a0Advances in neural information processing systems","author":"Santurkar S.","year":"2018","unstructured":"Santurkar, S., Tsipras, D., Ilyas, A., & Madry, A. How does batch normalization help optimization?\u00a0Advances in neural information processing systems, 2018, 31."},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3257562"},{"key":"e_1_3_3_1_26_2","first-page":"392","volume-title":"N. D. Federated Learning Using Variable Partial Training for Brain Tumor Segmentation. In\u00a0International MICCAI Brainlesion Workshop","author":"Tuladhar A.","year":"2021","unstructured":"Tuladhar, A., Tyagi, L., Souza, R., & Forkert, N. D. Federated Learning Using Variable Partial Training for Brain Tumor Segmentation. In\u00a0International MICCAI Brainlesion Workshop, Cham: Springer International Publishing. 2021, September. (pp. 392-404)."},{"key":"e_1_3_3_1_27_2","volume-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization.\u00a0Advances in neural information processing systems","author":"Wang J.","year":"2020","unstructured":"Wang, J., Liu, Q., Liang, H., Joshi, G., & Poor, H. V. Tackling the objective inconsistency problem in heterogeneous federated optimization.\u00a0Advances in neural information processing systems, 2020, 33, 7611-7623."},{"key":"e_1_3_3_1_28_2","volume-title":"National Brain Tumor Registry of China (NBTRC) statistical report of primary brain tumors diagnosed in China in years 2019\u20132020.\u00a0The Lancet Regional Health\u2013Western Pacific","author":"Xiao D.","year":"2023","unstructured":"Xiao, D., Yan, C., Li, D., Xi, T., Liu, X., Zhu, D., ... & Zhang, L. National Brain Tumor Registry of China (NBTRC) statistical report of primary brain tumors diagnosed in China in years 2019\u20132020.\u00a0The Lancet Regional Health\u2013Western Pacific, 2023, 34."},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-61609-0_60"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1108\/IJWIS-04-2022-0080"}],"event":{"name":"ISAIMS 2024: 2024 5th International Symposium on Artificial Intelligence for Medicine Science","location":"Amsterdam Netherlands","acronym":"ISAIMS 2024"},"container-title":["Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706890.3706891","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3706890.3706891","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:46Z","timestamp":1750295926000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706890.3706891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":30,"alternative-id":["10.1145\/3706890.3706891","10.1145\/3706890"],"URL":"https:\/\/doi.org\/10.1145\/3706890.3706891","relation":{},"subject":[],"published":{"date-parts":[[2024,8,13]]},"assertion":[{"value":"2025-01-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}