{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:10:48Z","timestamp":1742915448666,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031530814"},{"type":"electronic","value":"9783031530821"}],"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\/978-3-031-53082-1_7","type":"book-chapter","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T18:02:52Z","timestamp":1706637772000},"page":"76-88","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Preserving Accuracy in\u00a0Federated Learning via\u00a0Equitable Model and\u00a0Efficient Aggregation"],"prefix":"10.1007","author":[{"given":"Muntazir","family":"Mehdi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaisha","family":"Makkar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myra","family":"Conway","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lakshit","family":"Sama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"issue":"18","key":"7_CR1","doi-asserted-by":"publisher","first-page":"6311","DOI":"10.3390\/s21186311","volume":"21","author":"E Brophy","year":"2021","unstructured":"Brophy, E., De Vos, M., Boylan, G., Ward, T.: Estimation of continuous blood pressure from PPG via a federated learning approach. Sensors 21(18), 6311 (2021)","journal-title":"Sensors"},{"issue":"10","key":"7_CR2","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","volume":"27","author":"I Dayan","year":"2021","unstructured":"Dayan, I., et al.: Federated learning for predicting clinical outcomes in patients with COVID-19. Nat. Med. 27(10), 1735\u20131743 (2021)","journal-title":"Nat. Med."},{"key":"7_CR3","unstructured":"Hill, P.: The rationale for learning communities and learning community models. Research Square (1985)"},{"key":"7_CR4","unstructured":"Jiang, J., Lu, Z.: Learning fairness in multi-agent systems. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"issue":"6","key":"7_CR5","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1038\/s42256-021-00337-8","volume":"3","author":"G Kaissis","year":"2021","unstructured":"Kaissis, G., et al.: End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3(6), 473\u2013484 (2021)","journal-title":"Nat. Mach. Intell."},{"issue":"14","key":"7_CR6","doi-asserted-by":"publisher","first-page":"16301","DOI":"10.1109\/JSEN.2021.3076767","volume":"21","author":"R Kumar","year":"2021","unstructured":"Kumar, R., et al.: Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging. IEEE Sens. J. 21(14), 16301\u201316314 (2021)","journal-title":"IEEE Sens. J."},{"issue":"3","key":"7_CR7","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 Sig. Process. Mag. 37(3), 50\u201360 (2020)","journal-title":"IEEE Sig. Process. Mag."},{"issue":"1","key":"7_CR8","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.1038\/s41598-022-07186-4","volume":"12","author":"A Linardos","year":"2022","unstructured":"Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., Lekadir, K.: Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease. Sci. Rep. 12(1), 3551 (2022)","journal-title":"Sci. Rep."},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Makkar, A., Santosh, K.C.: Securefed: Federated learning empowered medical imaging technique to detect COVID-19 using chest x-rays. Research Square (2022)","DOI":"10.21203\/rs.3.rs-1943509\/v1"},{"key":"7_CR10","unstructured":"Marfoq, O., Neglia, G., Vidal, R., Kameni, L.: Personalized federated learning through local memorization. In International Conference on Machine Learning, pp. 15070\u201315092. PMLR (2022)"},{"key":"7_CR11","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp. 1273\u20131282. PMLR (2017)"},{"issue":"1","key":"7_CR12","doi-asserted-by":"publisher","first-page":"2362","DOI":"10.1038\/s41598-019-39071-y","volume":"9","author":"X Min","year":"2019","unstructured":"Min, X., Bin, Yu., Wang, F.: Predictive modeling of the hospital readmission risk from patients\u2019 claims data using machine learning: a case study on copd. Sci. Rep. 9(1), 2362 (2019)","journal-title":"Sci. Rep."},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.ymeth.2022.03.005","volume":"204","author":"A Nandi","year":"2022","unstructured":"Nandi, A., Xhafa, F.: A federated learning method for real-time emotion state classification from multi-modal streaming. Methods 204, 340\u2013347 (2022)","journal-title":"Methods"},{"issue":"20","key":"7_CR14","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1056\/NEJMoa1901183","volume":"381","author":"MV Perez","year":"2019","unstructured":"Perez, M.V., et al.: Large-scale assessment of a smartwatch to identify atrial fibrillation. New Engl. J. Med. 381(20), 1909\u20131917 (2019)","journal-title":"New Engl. J. Med."},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-031-08637-3_3","volume-title":"Interpretable Cognitive Internet of Things for Healthcare","author":"K Sahinbas","year":"2012","unstructured":"Sahinbas, K., Catak, F.O.: Secure multi-party computation-based privacy-preserving data analysis in healthcare IoT systems. In: Kose, U., Gupta, D., Khanna, A., Rodrigues, J.J.P.C. (eds.) Interpretable Cognitive Internet of Things for Healthcare, pp. 57\u201372. Springer, Cham (2012). https:\/\/doi.org\/10.1007\/978-3-031-08637-3_3"},{"key":"7_CR16","doi-asserted-by":"publisher","first-page":"8693","DOI":"10.1109\/ACCESS.2022.3141913","volume":"10","author":"BC Tedeschini","year":"2022","unstructured":"Tedeschini, B.C., et al.: Decentralized federated learning for healthcare networks: a case study on tumor segmentation. IEEE Access 10, 8693\u20138708 (2022)","journal-title":"IEEE Access"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Yoo, J.H., et al.: Personalized federated learning with clustering: non-IID heart rate variability data application. In: 2021 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1046\u20131051. IEEE (2021)","DOI":"10.1109\/ICTC52510.2021.9620852"},{"key":"7_CR18","unstructured":"Zhang, J., Li, C., Robles-Kelly, A., Kankanhalli, M.: Hierarchically fair federated learning. arXiv preprint arXiv:2004.10386 (2020)"},{"issue":"21","key":"7_CR19","doi-asserted-by":"publisher","first-page":"15884","DOI":"10.1109\/JIOT.2021.3056185","volume":"8","author":"W Zhang","year":"2021","unstructured":"Zhang, W., et al.: Dynamic-fusion-based federated learning for COVID-19 detection. IEEE Internet Things J. 8(21), 15884\u201315891 (2021)","journal-title":"IEEE Internet Things J."}],"container-title":["Communications in Computer and Information Science","Recent Trends in Image Processing and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53082-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T18:08:12Z","timestamp":1706638092000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53082-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031530814","9783031530821"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53082-1_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RTIP2R","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Recent Trends in Image Processing and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Derby","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":"7 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rtip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rtip2r-conference.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT, Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"216","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.39","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.79","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}