{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:35:57Z","timestamp":1742913357697,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":68,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819923557"},{"type":"electronic","value":"9789819923564"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-2356-4_31","type":"book-chapter","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T13:03:30Z","timestamp":1683896610000},"page":"390-404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Heterogeneous Federated Learning"],"prefix":"10.1007","author":[{"given":"Yue","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghui","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanju","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingzhong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,13]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1016\/j.future.2021.10.016","volume":"128","author":"AA Abdellatif","year":"2022","unstructured":"Abdellatif, A.A., et al.: Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data. Fut. Gener. Comput. Syst. 128, 406\u2013419 (2022)","journal-title":"Fut. Gener. Comput. Syst."},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Abdelmoniem, A.M., Ho, C.Y., Papageorgiou, P., Canini, M.: Empirical analysis of federated learning in heterogeneous environments. In: Proceedings of the 2nd European Workshop on Machine Learning and Systems, pp. 1\u20139 (2022)","DOI":"10.1145\/3517207.3526969"},{"key":"31_CR3","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra Perez, X., Reyes Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 437\u2013442 (2013)"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Baumgartner, J., Zannettou, S., Keegan, B., Squire, M., Blackburn, J.: The pushshift reddit dataset. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 830\u2013839 (2020)","DOI":"10.1609\/icwsm.v14i1.7347"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175\u20131191 (2017)","DOI":"10.1145\/3133956.3133982"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Cao, X., Li, Z., Yu, H., Sun, G.: COFED: cross-silo heterogeneous federated multi-task learning via co-training. arXiv preprint arXiv:2202.08603 (2022)","DOI":"10.1016\/j.knosys.2023.110347"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Chen, L.Y., Chiu, T.C., Pang, A.C., Cheng, L.C.: Fedequal: defending model poisoning attacks in heterogeneous federated learning. In: 2021 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/GLOBECOM46510.2021.9685082"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Cho, Y.J., Manoel, A., Joshi, G., Sim, R., Dimitriadis, D.: Heterogeneous ensemble knowledge transfer for training large models in federated learning. arXiv preprint arXiv:2204.12703 (2022)","DOI":"10.24963\/ijcai.2022\/399"},{"key":"31_CR9","doi-asserted-by":"publisher","unstructured":"Cohen, G., Afshar, S., Tapson, J., van Schaik, A.: Emnist: extending mnist to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2921\u20132926 (2017). https:\/\/doi.org\/10.1109\/IJCNN.2017.7966217","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Cui, Y., Cao, K., Zhou, J., Wei, T.: HELCFL: high-efficiency and low-cost federated learning in heterogeneous mobile-edge computing. In: 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1227\u20131232. IEEE (2022)","DOI":"10.23919\/DATE54114.2022.9774662"},{"issue":"2","key":"31_CR11","first-page":"2","volume":"7","author":"TE De Campos","year":"2009","unstructured":"De Campos, T.E., Babu, B.R., Varma, M.: Character recognition in natural images. VISAPP 7(2), 2 (2009)","journal-title":"VISAPP"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Duan, M., et al.: Fedgroup: efficient federated learning via decomposed similarity-based clustering. In: 2021 IEEE International Conference on Parallel & Distributed Processing with Applications, pp. 228\u2013237. IEEE (2021)","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00042"},{"issue":"4","key":"31_CR13","doi-asserted-by":"publisher","first-page":"2372","DOI":"10.1109\/TCOMM.2022.3151126","volume":"70","author":"AR Elkordy","year":"2022","unstructured":"Elkordy, A.R., Avestimehr, A.S.: Heterosag: secure aggregation with heterogeneous quantization in federated learning. IEEE Trans. Commun. 70(4), 2372\u20132386 (2022)","journal-title":"IEEE Trans. Commun."},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Gao, D., Liu, Y., Huang, A., Ju, C., Yu, H., Yang, Q.: Privacy-preserving heterogeneous federated transfer learning. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 2552\u20132559. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9005992"},{"key":"31_CR15","doi-asserted-by":"publisher","unstructured":"Gao, Z., Duan, Y., Yang, Y., Rui, L., Zhao, C.: Fedsec: a robust differential private federated learning framework in heterogeneous networks. In: 2022 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1868\u20131873 (2022). https:\/\/doi.org\/10.1109\/WCNC51071.2022.9771929","DOI":"10.1109\/WCNC51071.2022.9771929"},{"key":"31_CR16","unstructured":"Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Processing 150 (2009)"},{"key":"31_CR17","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/JSTSP.2021.3126174","volume":"16","author":"K Guo","year":"2021","unstructured":"Guo, K., Chen, Z., Yang, H.H., Quek, T.Q.: Dynamic scheduling for heterogeneous federated learning in private 5g edge networks. IEEE J. Sel. Topics Signal Process. 16, 26\u201340 (2021)","journal-title":"IEEE J. Sel. Topics Signal Process."},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"Guo, Y., Wang, Q., Ji, T., Wang, X., Li, P.: Resisting distributed backdoor attacks in federated learning: a dynamic norm clipping approach. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 1172\u20131182. IEEE (2021)","DOI":"10.1109\/BigData52589.2021.9671910"},{"key":"31_CR19","unstructured":"Hin, C.Y., Edith, N.: Fedhe: heterogeneous models and communication-efficient federated learning. arXiv preprint arXiv:2110.09910 (2021)"},{"issue":"10","key":"31_CR20","doi-asserted-by":"publisher","first-page":"2612","DOI":"10.1109\/TPDS.2022.3148113","volume":"33","author":"M Hu","year":"2022","unstructured":"Hu, M., Wu, D., Zhou, Y., Chen, X., Chen, M.: Incentive-aware autonomous client participation in federated learning. IEEE Trans. Parallel Distrib. Syst. 33(10), 2612\u20132627 (2022)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"31_CR21","doi-asserted-by":"crossref","unstructured":"Kachuee, M., Fazeli, S., Sarrafzadeh, M.: ECG heartbeat classification: a deep transferable representation. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 443\u2013444. IEEE (2018)","DOI":"10.1109\/ICHI.2018.00092"},{"key":"31_CR22","doi-asserted-by":"crossref","unstructured":"Kanaparthy, S., Padala, M., Damle, S., Gujar, S.: Fair federated learning for heterogeneous face data. arXiv preprint arXiv:2109.02351 (2021)","DOI":"10.1145\/3493700.3493750"},{"key":"31_CR23","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":"31_CR24","doi-asserted-by":"crossref","unstructured":"Karkkainen, K., Joo, J.: Fairface: face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1548\u20131558 (2021)","DOI":"10.1109\/WACV48630.2021.00159"},{"key":"31_CR25","doi-asserted-by":"crossref","unstructured":"Kinnunen, T., Kamarainen, J.K., Lensu, L., Lankinen, J., K\u00e4vi\u00e4inen, H.: Making visual object categorization more challenging: randomized caltech-101 data set. In: 2010 20th International Conference on Pattern Recognition, pp. 476\u2013479. IEEE (2010)","DOI":"10.1109\/ICPR.2010.124"},{"key":"31_CR26","unstructured":"Kohavi, R.: Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid. In: KDD, vol. 96, pp. 202\u2013207 (1996)"},{"key":"31_CR27","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. University of Toronto (2009)"},{"issue":"11","key":"31_CR28","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"31_CR29","unstructured":"Li, A., Set al.: Lotteryfl: personalized and communication-efficient federated learning with lottery ticket hypothesis on non-iid datasets. arXiv preprint arXiv:2008.03371 (2020)"},{"key":"31_CR30","doi-asserted-by":"crossref","unstructured":"Li, A., Sun, J., Zeng, X., Zhang, M., Li, H., Chen, Y.: Fedmask: joint computation and communication-efficient personalized federated learning via heterogeneous masking. In: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, pp. 42\u201355 (2021)","DOI":"10.1145\/3485730.3485929"},{"key":"31_CR31","unstructured":"Li, D., Wang, J.: FEDMD: heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581 (2019)"},{"key":"31_CR32","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5542\u20135550 (2017)","DOI":"10.1109\/ICCV.2017.591"},{"key":"31_CR33","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Fedsae: a novel self-adaptive federated learning framework in heterogeneous systems. In: 2021 International Joint Conference on Neural Networks (IJCNN) (2021)","DOI":"10.1109\/IJCNN52387.2021.9533876"},{"key":"31_CR34","doi-asserted-by":"crossref","unstructured":"Li, L., Shi, D., Hou, R., Li, H., Pan, M., Han, Z.: To talk or to work: flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications, pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/INFOCOM42981.2021.9488839"},{"issue":"3","key":"31_CR35","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. Maga. 37(3), 50\u201360 (2020)","journal-title":"IEEE Signal Process. Maga."},{"key":"31_CR36","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."},{"issue":"3","key":"31_CR37","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1109\/JIOT.2021.3088867","volume":"9","author":"X Li","year":"2021","unstructured":"Li, X., Li, Y., Li, S., Zhou, Y., Chen, C., Zheng, Z.: A unified federated DNNs framework for heterogeneous mobile devices. IEEE Internet Things J. 9(3), 1737\u20131748 (2021)","journal-title":"IEEE Internet Things J."},{"key":"31_CR38","unstructured":"Li, Y., Zhou, W., Wang, H., Mi, H., Hospedales, T.M.: Fedh2l: federated learning with model and statistical heterogeneity. arXiv preprint arXiv:2101.11296 (2021)"},{"key":"31_CR39","unstructured":"Liu, Y., Zhang, L., Ge, N., Li, G.: A systematic literature review on federated learning: from a model quality perspective. arXiv preprint arXiv:2012.01973 (2020)"},{"key":"31_CR40","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730\u20133738 (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"31_CR41","doi-asserted-by":"publisher","first-page":"6058","DOI":"10.1109\/JIOT.2021.3110908","volume":"9","author":"X Lu","year":"2021","unstructured":"Lu, X., Liao, Y., Liu, C., Lio, P., Hui, P.: Heterogeneous model fusion federated learning mechanism based on model mapping. IEEE Internet Things J. 9, 6058\u20136068 (2021)","journal-title":"IEEE Internet Things J."},{"key":"31_CR42","doi-asserted-by":"crossref","unstructured":"Luo, J., Yang, J., Ye, X., Guo, X., Zhao, W.: Fedskel: efficient federated learning on heterogeneous systems with skeleton gradients update. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3283\u20133287 (2021)","DOI":"10.1145\/3459637.3482107"},{"issue":"4","key":"31_CR43","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1109\/MNET.001.1900506","volume":"34","author":"C Ma","year":"2020","unstructured":"Ma, C., et al.: On safeguarding privacy and security in the framework of federated learning. IEEE Netw. 34(4), 242\u2013248 (2020)","journal-title":"IEEE Netw."},{"issue":"12","key":"31_CR44","doi-asserted-by":"publisher","first-page":"3654","DOI":"10.1109\/JSAC.2021.3118435","volume":"39","author":"Q Ma","year":"2021","unstructured":"Ma, Q., Xu, Y., Xu, H., Jiang, Z., Huang, L., Huang, H.: FEDSA: a semi-asynchronous federated learning mechanism in heterogeneous edge computing. IEEE J. Sel. Areas Commun. 39(12), 3654\u20133672 (2021)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"31_CR45","doi-asserted-by":"publisher","first-page":"103561","DOI":"10.1016\/j.csi.2021.103561","volume":"80","author":"X Ma","year":"2022","unstructured":"Ma, X., Zhou, Y., Wang, L., Miao, M.: Privacy-preserving byzantine-robust federated learning. Comput. Stand. Interfaces 80, 103561 (2022)","journal-title":"Comput. Stand. Interfaces"},{"key":"31_CR46","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)"},{"key":"31_CR47","doi-asserted-by":"publisher","unstructured":"Melis, L., Song, C., De Cristofaro, E., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 691\u2013706 (2019). https:\/\/doi.org\/10.1109\/SP.2019.00029","DOI":"10.1109\/SP.2019.00029"},{"key":"31_CR48","unstructured":"Shakespeare, W.: The Complete Works of William Shakespeare. Race Point Publishing (2014)"},{"key":"31_CR49","unstructured":"Shamir, O., Srebro, N., Zhang, T.: Communication-efficient distributed optimization using an approximate newton-type method. In: International Conference on Machine Learning, pp. 1000\u20131008. PMLR (2014)"},{"key":"31_CR50","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"},{"key":"31_CR51","unstructured":"Shin, J., Li, Y., Liu, Y., Lee, S.J.: Sample selection with deadline control for efficient federated learning on heterogeneous clients. arXiv preprint arXiv:2201.01601 (2022)"},{"issue":"1","key":"31_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2019.39","volume":"6","author":"NJ Stevenson","year":"2019","unstructured":"Stevenson, N.J., Tapani, K., Lauronen, L., Vanhatalo, S.: A dataset of neonatal EEG recordings with seizure annotations. Sci. Data 6(1), 1\u20138 (2019)","journal-title":"Sci. Data"},{"key":"31_CR53","doi-asserted-by":"crossref","unstructured":"Sun, C., Jiang, T., Zonouz, S., Pompili, D.: Fed2kd: heterogeneous federated learning for pandemic risk assessment via two-way knowledge distillation. In: 2022 17th Wireless On-Demand Network Systems and Services Conference (WONS), pp. 1\u20138. IEEE (2022)","DOI":"10.23919\/WONS54113.2022.9764443"},{"key":"31_CR54","doi-asserted-by":"crossref","unstructured":"Tan, Y., et al.: Fedproto: federated prototype learning across heterogeneous clients. In: AAAI Conference on Artificial Intelligence, vol. 1 (2022)","DOI":"10.1609\/aaai.v36i8.20819"},{"issue":"3","key":"31_CR55","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/s00138-011-0391-3","volume":"25","author":"R Timofte","year":"2014","unstructured":"Timofte, R., Zimmermann, K., Van Gool, L.: Multi-view traffic sign detection, recognition, and 3d localisation. Mach. Vision Appl. 25(3), 633\u2013647 (2014)","journal-title":"Mach. Vision Appl."},{"key":"31_CR56","unstructured":"Van Oord, A., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. In: International Conference on Machine Learning, pp. 1747\u20131756. PMLR (2016)"},{"key":"31_CR57","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5018\u20135027 (2017)","DOI":"10.1109\/CVPR.2017.572"},{"key":"31_CR58","doi-asserted-by":"crossref","unstructured":"Wang, D., et al.: CFL-HC: a coded federated learning framework for heterogeneous computing scenarios. In: 2021 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/GLOBECOM46510.2021.9685962"},{"key":"31_CR59","doi-asserted-by":"publisher","first-page":"5234","DOI":"10.1109\/TSP.2021.3106104","volume":"69","author":"J Wang","year":"2021","unstructured":"Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: A novel framework for the analysis and design of heterogeneous federated learning. IEEE Trans. Signal Process. 69, 5234\u20135249 (2021)","journal-title":"IEEE Trans. Signal Process."},{"key":"31_CR60","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)"},{"key":"31_CR61","unstructured":"Xu, C., Qu, Y., Xiang, Y., Gao, L.: Asynchronous federated learning on heterogeneous devices: a survey. arXiv preprint arXiv:2109.04269 (2021)"},{"key":"31_CR62","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1109\/TIFS.2019.2929409","volume":"15","author":"G Xu","year":"2019","unstructured":"Xu, G., Li, H., Liu, S., Yang, K., Lin, X.: Verifynet: secure and verifiable federated learning. IEEE Trans. Inf. For. Secur. 15, 911\u2013926 (2019)","journal-title":"IEEE Trans. Inf. For. Secur."},{"key":"31_CR63","doi-asserted-by":"crossref","unstructured":"Zaccone, R., Rizzardi, A., Caldarola, D., Ciccone, M., Caputo, B.: Speeding up heterogeneous federated learning with sequentially trained superclients. arXiv preprint arXiv:2201.10899 (2022)","DOI":"10.1109\/ICPR56361.2022.9956084"},{"key":"31_CR64","doi-asserted-by":"crossref","unstructured":"Zeng, H., Zhou, T., Guo, Y., Cai, Z., Liu, F.: Fedcav: contribution-aware model aggregation on distributed heterogeneous data in federated learning. In: 50th International Conference on Parallel Processing, pp. 1\u201310 (2021)","DOI":"10.1145\/3472456.3472504"},{"key":"31_CR65","unstructured":"Zhang, H., Kim, J.: Towards a federated learning framework for heterogeneous devices of internet of things. arXiv preprint arXiv:2105.14675 (2021)"},{"key":"31_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, F., Zhang, Z., Li, Q., Wang, C., Wu, J.: Enabling execution assurance of federated learning at untrusted participants. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 1877\u20131886. IEEE (2020)","DOI":"10.1109\/INFOCOM41043.2020.9155414"},{"key":"31_CR67","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Song, Y., Qi, H.: Age progression\/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5810\u20135818 (2017)","DOI":"10.1109\/CVPR.2017.463"},{"issue":"6","key":"31_CR68","doi-asserted-by":"publisher","first-page":"5308","DOI":"10.1109\/TVT.2021.3077893","volume":"70","author":"X Zhou","year":"2021","unstructured":"Zhou, X., et al.: Two-layer federated learning with heterogeneous model aggregation for 6G supported internet of vehicles. IEEE Trans. Veh. Technol. 70(6), 5308\u20135317 (2021)","journal-title":"IEEE Trans. Veh. Technol."}],"container-title":["Communications in Computer and Information Science","Computer Supported Cooperative Work and Social Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-2356-4_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T13:03:54Z","timestamp":1683896634000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-2356-4_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819923557","9789819923564"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-2356-4_31","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ChineseCSCW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF Conference on Computer Supported Cooperative Work  and Social Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Datong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"chinesecscw2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.scholat.com\/ccscw\/2022","order":11,"name":"conference_url","label":"Conference URL","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"211","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":"60","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":"30","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":"28% - 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":"4","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)"}}]}}