{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:51:52Z","timestamp":1743022312104,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031545306"},{"type":"electronic","value":"9783031545313"}],"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\/978-3-031-54531-3_4","type":"book-chapter","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T07:06:29Z","timestamp":1708585589000},"page":"59-78","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FedECCR: Federated Learning Method with\u00a0Encoding Comparison and\u00a0Classification Rectification"],"prefix":"10.1007","author":[{"given":"Yan","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8928-6106","authenticated-orcid":false,"given":"Hui","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2199-3879","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1417-7005","authenticated-orcid":false,"given":"Beibei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0346-2051","authenticated-orcid":false,"given":"Mingyao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0241-0727","authenticated-orcid":false,"given":"Jilin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4632-2537","authenticated-orcid":false,"given":"YongJian","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"2389","DOI":"10.1007\/s11036-021-01829-y","volume":"26","author":"X Wang","year":"2021","unstructured":"Wang, X., Gao, H., Huang, K.: Artificial intelligence in collaborative computing. Mobile Netw. Appl. 26, 2389\u20132391 (2021). https:\/\/doi.org\/10.1007\/s11036-021-01829-y","journal-title":"Mobile Netw. Appl."},{"key":"4_CR2","doi-asserted-by":"publisher","unstructured":"Yang, J., Zheng, J., Zhang, Z., Chen, Q.I., Wong, D.S., Li, Y.: Security of federated learning for cloud-edge intelligence collaborative computing. Int. J. Intell. Syst., 9290\u20139308 (2022). https:\/\/doi.org\/10.1002\/int.22992","DOI":"10.1002\/int.22992"},{"key":"4_CR3","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.: Communication-efficient learning of deep networks from decentralized data. arXiv:\u00a0Learning (2016)"},{"key":"4_CR4","unstructured":"Hard, A., et al.: Federated learning for mobile keyboard prediction. arXiv:\u00a0Computation and Language (2018)"},{"key":"4_CR5","unstructured":"Geyer, R.C., Klein, T., Nabi, M.: Differentially private federated learning: a client level perspective. Cornell University - arXiv (2017)"},{"key":"4_CR6","unstructured":"Tan, Y., Long, G., Liu, L., Zhou, T., Jiang, J.: FedProto: federated prototype learning over heterogeneous devices. arXiv:\u00a0Learning (2021)"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Reynolds, D.A.: Gaussian Mixture Models (2009)","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"4_CR8","unstructured":"Yan, Y., Zhu, L.: A Simple Data Augmentation for Feature Distribution Skewed Federated Learning (2023)"},{"key":"4_CR9","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-IID data. Cornell University - arXiv (2018)"},{"key":"4_CR10","unstructured":"Tuor, T., Wang, S., Ko, B., Liu, C., Leung, K.K.: Overcoming noisy and irrelevant data in federated learning. arXiv:\u00a0Learning (2020)"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Yoshida, N., Nishio, T., Morikura, M., Yamamoto, K., Yonetani, R.: Hybrid-FL: Cooperative Learning Mechanism Using Non-IID Data in Wireless Networks (2019)","DOI":"10.1109\/ICC40277.2020.9149323"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Wicaksana, J., et al.: FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation (2022)","DOI":"10.1109\/TMI.2022.3233405"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Seol, M., Kim, T.: Performance enhancement in federated learning by reducing class imbalance of non-IID data. Sensors, 1152 (2023)","DOI":"10.3390\/s23031152"},{"key":"4_CR14","unstructured":"Shin, M., Hwang, C., Kim, J., Park, J., Bennis, M., Kim, S.-L.: XOR mixup: privacy-preserving data augmentation for one-shot federated learning. Cornell University - arXiv (2020)"},{"key":"4_CR15","unstructured":"Jeong, E., Oh, S., Park, J., Kim, H., Bennis, M., Kim, S.-L.: Multi-hop federated private data augmentation with sample compression. arXiv:\u00a0Learning (2019)"},{"key":"4_CR16","unstructured":"Karimireddy, S., Kale, S., Mohri, M., Reddi, S.J., Stich, S.U., Suresh, A.: SCAFFOLD: stochastic controlled averaging for federated learning. In: International Conference on Machine Learning (2020)"},{"key":"4_CR17","unstructured":"Gao, L., Fu, H., Li, L., Chen, Y., Xu, M., Xu, C.-Z.: FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction"},{"key":"4_CR18","unstructured":"Liu, Y., Sun, Y., Ding, Z., Shen, L., Liu, B., Tao, D.: Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach (2023)"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Li, B., Schmidt, M.N., Alstr\u00f8m, T.S., Stich, S.U.: Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data (2022)","DOI":"10.1109\/CVPR52729.2023.00386"},{"key":"4_CR20","unstructured":"Li, T., Sahu, A., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. arXiv:\u00a0Learning (2018)"},{"key":"4_CR21","unstructured":"Shoham, N., et al.: Overcoming forgetting in federated learning on non-IID data. Cornell University - arXiv (2019)"},{"key":"4_CR22","doi-asserted-by":"publisher","unstructured":"Yao, X., Sun, L.: Continual local training for better initialization of federated models. In: 2020 IEEE International Conference on Image Processing (ICIP) (2020). https:\/\/doi.org\/10.1109\/icip40778.2020.9190968","DOI":"10.1109\/icip40778.2020.9190968"},{"key":"4_CR23","unstructured":"Li, H., Krishnan, A., Wu, J., Kolouri, S., Pilly, P.K., Braverman, V.: Lifelong learning with sketched structural regularization. Cornell University - arXiv (2021)"},{"key":"4_CR24","doi-asserted-by":"publisher","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. In: Proceedings of the National Academy of Sciences, pp. 3521\u20133526 (2017). https:\/\/doi.org\/10.1073\/pnas.1611835114","DOI":"10.1073\/pnas.1611835114"},{"key":"4_CR25","doi-asserted-by":"publisher","unstructured":"Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01057","DOI":"10.1109\/cvpr46437.2021.01057"},{"key":"4_CR26","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. Cornell University - arXiv (2020)"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Vanschoren, J.: Meta-learning: a survey. arXiv:\u00a0Learning (2018)","DOI":"10.1007\/978-3-030-05318-5_2"},{"key":"4_CR28","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Yang, Q.: An overview of multi-task learning. Natl. Sci. Rev., 30\u201343 (2018). https:\/\/doi.org\/10.1093\/nsr\/nwx105","DOI":"10.1093\/nsr\/nwx105"},{"key":"4_CR29","doi-asserted-by":"publisher","unstructured":"Yang, L., Huang, J., Lin, W., Cao, J.: Personalized federated learning on non-IID data via group-based meta-learning. ACM Trans. Knowl. Discov. Data., 1\u201320 (2023). https:\/\/doi.org\/10.1145\/3558005","DOI":"10.1145\/3558005"},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"He, C., Ceyani, E., Balasubramanian, K., Annavaram, M., Avestimehr, A.S.: SpreadGNN: serverless multi-task federated learning for graph neural networks. Cornell University - arXiv (2021)","DOI":"10.1609\/aaai.v36i6.20643"},{"key":"4_CR31","unstructured":"Mu, X., et al.: FedProc: prototypical contrastive federated learning on non-IID data. arXiv:\u00a0Learning (2021)"},{"key":"4_CR32","unstructured":"Miller, J.W., Harrison, M.T.: Mixture models with a prior on the number of components. arXiv:\u00a0Methodology (2015)"},{"key":"4_CR33","unstructured":"Hsu, H., Qi, H., Brown, M.: Measuring the effects of non-identical data distribution for federated visual classification. arXiv:\u00a0Learning (2019)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Collaborative Computing: Networking, Applications and Worksharing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-54531-3_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T07:09:43Z","timestamp":1708585783000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-54531-3_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031545306","9783031545313"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-54531-3_4","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CollaborateCom","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Collaborative Computing: Networking, Applications and Worksharing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"4 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"colcom2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Cony +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"176","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":"72","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":"41% - 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":"3","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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}