{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:14:36Z","timestamp":1743041676271,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819713349"},{"type":"electronic","value":"9789819713356"}],"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\/978-981-97-1335-6_26","type":"book-chapter","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T19:02:17Z","timestamp":1709665337000},"page":"297-306","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TMN: An Efficient Robust Aggregator for\u00a0Federated Learning"],"prefix":"10.1007","author":[{"given":"Anees Ur Rehman","family":"Hashmi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed El-Amine","family":"Azz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.ejmp.2021.04.016","volume":"83","author":"A Barrag\u00e1n-Montero","year":"2021","unstructured":"Barrag\u00e1n-Montero, A., et al.: Artificial intelligence and machine learning for medical imaging: a technology review. Physica Med. 83, 242\u2013256 (2021)","journal-title":"Physica Med."},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Bansal, A., Sharma, R., Kathuria, M.: A systematic review on data scarcity problem in deep learning: solution and applications. ACM Comput. Surv. (CSUR) 54(10s), 1\u201329 (2022)","DOI":"10.1145\/3502287"},{"key":"26_CR3","unstructured":"US Department of Health and Human Services. Hipaa (2020). https:\/\/www.hhs.gov\/hipaa\/index.html"},{"issue":"1","key":"26_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2458-14-1144","volume":"14","author":"WG Van Panhuis","year":"2014","unstructured":"Van Panhuis, W.G., et al.: A systematic review of barriers to data sharing in public health. BMC Public Health 14(1), 1\u20139 (2014)","journal-title":"BMC Public Health"},{"key":"26_CR5","first-page":"17","volume":"11","author":"A Kaushal","year":"2020","unstructured":"Kaushal, A., Altman, R., Langlotz, C.: Health care AI systems are biased. Sci. Am. 11, 17 (2020)","journal-title":"Sci. Am."},{"key":"26_CR6","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Aguera y\u00a0Arcas, B.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"26_CR7","unstructured":"Guan, H., Liu, M.: Federated learning for medical image analysis: a survey. arXiv preprint arXiv:2306.05980 (2023)"},{"key":"26_CR8","unstructured":"Zhang, J., Li, C., Qi, J., He, J.: A survey on class imbalance in federated learning. arXiv preprint arXiv:2303.11673 (2023)"},{"key":"26_CR9","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2021.07.098","volume":"465","author":"H Zhu","year":"2021","unstructured":"Zhu, H., Jinjin, X., Liu, S., Jin, Y.: Federated learning on non-IID data: a survey. Neurocomputing 465, 371\u2013390 (2021)","journal-title":"Neurocomputing"},{"key":"26_CR10","unstructured":"Fu, S., Xie, C., Li, B., Chen, Q.: Attack-resistant federated learning with residual-based reweighting. arXiv preprint arXiv:1912.11464 (2019)"},{"issue":"3","key":"26_CR11","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1145\/2402.322398","volume":"30","author":"L Lamport","year":"1983","unstructured":"Lamport, L.: The weak byzantine generals problem. J. ACM (JACM) 30(3), 668\u2013676 (1983)","journal-title":"J. ACM (JACM)"},{"issue":"3","key":"26_CR12","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., Kumar Sahu, A., 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":"26_CR13","unstructured":"Wang, H., Yurochkin, H., Sun, Y., Papailiopoulos, D., Khazaeni, Y.: Federated learning with matched averaging. arXiv preprint arXiv:2002.06440 (2020)"},{"key":"26_CR14","first-page":"7611","volume":"33","author":"J Wang","year":"2020","unstructured":"Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: Tackling the objective inconsistency problem in heterogeneous federated optimization. Adv. Neural. Inf. Process. Syst. 33, 7611\u20137623 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"26_CR15","unstructured":"Blanchard, P., Mhamdi, El.M., Guerraoui, R., Stainer, J.: Machine learning with adversaries: Byzantine tolerant gradient descent. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"26_CR16","unstructured":"Yin, D., Chen, Y., Kannan, R., Bartlett, P.: Byzantine-robust distributed learning: towards optimal statistical rates. In: International Conference on Machine Learning, pp. 5650\u20135659. PMLR, (2018)"},{"key":"26_CR17","doi-asserted-by":"publisher","unstructured":"Alkhunaizi, N., Kamzolov, D., Tak\u00e1\u010d, M., Nandakumar, K.: Suppressing poisoning attacks on federated learning for medical imaging. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part VIII, pp. 673\u2013683. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_64","DOI":"10.1007\/978-3-031-16452-1_64"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhao, Y., Botta, N., Ionescu, C., Hu, X.: COPOD: copula-based outlier detection. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 1118\u20131123. IEEE (2020)","DOI":"10.1109\/ICDM50108.2020.00135"},{"issue":"1","key":"26_CR19","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"}],"container-title":["Lecture Notes in Electrical Engineering","Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-1335-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T19:07:22Z","timestamp":1709665642000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-1335-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819713349","9789819713356"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-1335-6_26","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Imaging and Computer-Aided Diagnosis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micad2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}