{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:34:32Z","timestamp":1742945672043,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031639883"},{"type":"electronic","value":"9783031639890"}],"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\/978-3-031-63989-0_11","type":"book-chapter","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T21:01:50Z","timestamp":1721336510000},"page":"223-238","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FedGCS: Addressing Class Imbalance in\u00a0Long-Tail Federated Learning"],"prefix":"10.1007","author":[{"given":"Guozheng","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiling","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhuang","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meihong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"key":"11_CR1","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"11_CR2","unstructured":"Chen, H.Y., Chao, W.L.: Fedbe: making bayesian model ensemble applicable to federated learning. arXiv preprint arXiv:2009.01974 (2020)"},{"key":"11_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-030-58526-6_41","volume-title":"Computer Vision \u2013 ECCV 2020","author":"P Chu","year":"2020","unstructured":"Chu, P., Bian, X., Liu, S., Ling, H.: Feature space augmentation for long-tailed data. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 694\u2013710. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_41"},{"issue":"1","key":"11_CR4","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/TPDS.2020.3009406","volume":"32","author":"M Duan","year":"2020","unstructured":"Duan, M., Liu, D., Chen, X., Liu, R., Tan, Y., Liang, L.: Self-balancing federated learning with global imbalanced data in mobile systems. IEEE Trans. Parallel Distrib. Syst. 32(1), 59\u201371 (2020)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Huang, W.: Exploring classification equilibrium in long-tailed object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3417\u20133426 (2021)","DOI":"10.1109\/ICCV48922.2021.00340"},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"6117","DOI":"10.1109\/TCSVT.2023.3255229","volume":"33","author":"H Hao","year":"2023","unstructured":"Hao, H., Xu, C., Zhang, W., Yang, S., Muntean, G.M.: Computing offloading with fairness guarantee: a deep reinforcement learning method. IEEE Trans. Circ. Syst. Video Technol. 33, 6117\u20136130 (2023)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Hao, H., Xu, C., Zhong, L., Muntean, G.M.: A multi-update deep reinforcement learning algorithm for edge computing service offloading. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 3256\u20133264 (2020)","DOI":"10.1145\/3394171.3413702"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"11_CR9","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"11_CR11","unstructured":"Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., Kim, S.L.: Communication-efficient on-device machine learning: federated distillation and augmentation under non-iid private data. arXiv preprint arXiv:1811.11479 (2018)"},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/978-3-031-20080-9_10","volume-title":"European Conference on Computer Vision","author":"CM Jiang","year":"2022","unstructured":"Jiang, C.M., Najibi, M., Qi, C.R., Zhou, Y., Anguelov, D.: Improving the intra-class long-tail in 3d detection via rare example mining. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13670D, pp. 158\u2013175. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-20080-9_10"},{"key":"11_CR13","unstructured":"Kang, B., Li, Y., Xie, S., Yuan, Z., Feng, J.: Exploring balanced feature spaces for representation learning. In: International Conference on Learning Representations (2020)"},{"key":"11_CR14","unstructured":"Kang, Bet al.: Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217 (2019)"},{"key":"11_CR15","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: Scaffold: stochastic controlled averaging for federated learning. In: International Conference on Machine Learning, pp. 5132\u20135143. PMLR (2020)"},{"key":"11_CR16","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"key":"11_CR17","unstructured":"Li, D., Wang, J.: Fedmd: heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581 (2019)"},{"issue":"3","key":"11_CR18","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 Signal Process. Mag. 37(3), 50\u201360 (2020)","journal-title":"IEEE Signal Process. Mag."},{"key":"11_CR19","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429\u2013450 (2020)","journal-title":"Proc. Mach. Learn. Syst."},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, T., Kang, B., Tang, S., Wang, C., Li, J., Feng, J.: Overcoming classifier imbalance for long-tail object detection with balanced group softmax. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10991\u201311000 (2020)","DOI":"10.1109\/CVPR42600.2020.01100"},{"key":"11_CR21","first-page":"2351","volume":"33","author":"T Lin","year":"2020","unstructured":"Lin, T., Kong, L., Stich, S.U., Jaggi, M.: Ensemble distillation for robust model fusion in federated learning. Adv. Neural. Inf. Process. Syst. 33, 2351\u20132363 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Liu, B., Li, H., Kang, H., Hua, G., Vasconcelos, N.: Gistnet: a geometric structure transfer network for long-tailed recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8209\u20138218 (2021)","DOI":"10.1109\/ICCV48922.2021.00810"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Liu, T., Xia, J., Ling, Z., Fu, X., Yu, S., Chen, M.: Efficient federated learning for aiot applications using knowledge distillation. IEEE Internet Things J. (2022)","DOI":"10.1109\/JIOT.2022.3229374"},{"key":"11_CR24","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"11_CR25","first-page":"4175","volume":"33","author":"J Ren","year":"2020","unstructured":"Ren, J., Yu, C., Ma, X., Zhao, H., Yi, S., et al.: Balanced meta-softmax for long-tailed visual recognition. Adv. Neural. Inf. Process. Syst. 33, 4175\u20134186 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"11_CR27","unstructured":"Sarkar, D., Narang, A., Rai, S.: Fed-focal loss for imbalanced data classification in federated learning. arXiv preprint arXiv:2011.06283 (2020)"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Shang, X., Lu, Y., Huang, G., Wang, H.: Federated learning on heterogeneous and long-tailed data via classifier re-training with federated features. arXiv preprint arXiv:2204.13399 (2022)","DOI":"10.24963\/ijcai.2022\/308"},{"issue":"1","key":"11_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-69250-1","volume":"10","author":"MJ Sheller","year":"2020","unstructured":"Sheller, M.J., et al.: Federated learning in medicine facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10(1), 1\u201312 (2020)","journal-title":"Sci. Rep."},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Shuai, X., Shen, Y., Jiang, S., Zhao, Z., Yan, Z., Xing, G.: Balancefl: addressing class imbalance in long-tail federated learning. In: 2022 21st ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pp. 271\u2013284. IEEE (2022)","DOI":"10.1109\/IPSN54338.2022.00029"},{"key":"11_CR32","first-page":"21394","volume":"33","author":"TC Dinh","year":"2020","unstructured":"Dinh, T.C., Tran, N., Nguyen, J.: Personalized federated learning with moreau envelopes. Adv. Neural Inf. Process. Syst. 33, 21394\u201321405 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"11_CR33","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"11_CR34","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., Khazaeni, Y.: Federated learning with matched averaging. arXiv preprint arXiv:2002.06440 (2020)"},{"key":"11_CR35","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":"11_CR36","doi-asserted-by":"crossref","unstructured":"Wang, L., Xu, S., Wang, X., Zhu, Q.: Addressing class imbalance in federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 10165\u201310173 (2021)","DOI":"10.1609\/aaai.v35i11.17219"},{"key":"11_CR37","doi-asserted-by":"publisher","first-page":"10795","DOI":"10.1109\/TPAMI.2023.3268118","volume":"45","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Kang, B., Hooi, B., Yan, S., Feng, J.: Deep long-tailed learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45, 10795\u201310816 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Zheng, W., Yan, L., Gou, C., Wang, F.Y.: Federated meta-learning for fraudulent credit card detection. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4654\u20134660 (2021)","DOI":"10.24963\/ijcai.2020\/642"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"Zhou, B., Cui, Q., Wei, X.S., Chen, Z.M.: BBN: bilateral-branch network with cumulative learning for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9719\u20139728 (2020)","DOI":"10.1109\/CVPR42600.2020.00974"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Mobile and Ubiquitous Systems: Computing, Networking and Services"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63989-0_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T21:08:44Z","timestamp":1721336924000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63989-0_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031639883","9783031639890"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63989-0_11","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":"19 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MobiQuitous","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Melbourne, VIC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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":"13 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mobiquitous2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mobiquitous.eai-conferences.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}