{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T22:38:51Z","timestamp":1776983931211,"version":"3.51.4"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032234469","type":"print"},{"value":"9783032234476","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-23447-6_26","type":"book-chapter","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T22:03:27Z","timestamp":1776981807000},"page":"544-562","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LPFL-RL: A Lightweight Privacy-Preserving Federated Learning Scheme with\u00a0Robustness Against Low-Quality Users in\u00a0Cloud-Edge Collaborative Environments"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1684-2880","authenticated-orcid":false,"given":"Feixiang","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiwen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixue","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhipeng","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"26_CR1","unstructured":"Blanchard, P., El\u00a0Mhamdi, E.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_CR2","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":"26_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Tan, W., Zhong, Y., Kang, Y., Yang, A., Weng, J.: Byzantine-robust and privacy-preserving federated learning with irregular participants. IEEE Internet Things J. (2024)","DOI":"10.1109\/JIOT.2024.3434660"},{"key":"26_CR4","unstructured":"Fang, M., Cao, X., Jia, J., Gong, N.: Local model poisoning attacks to $$\\{$$Byzantine-robust$$\\}$$ federated learning. In: 29th USENIX Security Symposium (USENIX Security 20), pp. 1605\u20131622 (2020)"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1322\u20131333 (2015)","DOI":"10.1145\/2810103.2813677"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Kanagavelu, R., et al.: Two-phase multi-party computation enabled privacy-preserving federated learning. In: 2020 20th IEEE\/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), pp. 410\u2013419. IEEE (2020)","DOI":"10.1109\/CCGrid49817.2020.00-52"},{"issue":"13","key":"26_CR7","doi-asserted-by":"publisher","first-page":"11590","DOI":"10.1109\/JIOT.2021.3130115","volume":"9","author":"Y Li","year":"2021","unstructured":"Li, Y., Li, H., Xu, G., Huang, X., Lu, R.: Efficient privacy-preserving federated learning with unreliable users. IEEE Internet Things J. 9(13), 11590\u201311603 (2021)","journal-title":"IEEE Internet Things J."},{"key":"26_CR8","unstructured":"Liang, P.P., et al.: Think locally, act globally: federated learning with local and global representations. arXiv preprint arXiv:2001.01523 (2020)"},{"issue":"2","key":"26_CR9","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1109\/TR.2022.3190932","volume":"72","author":"W Liang","year":"2022","unstructured":"Liang, W., et al.: PDPChain: a consortium blockchain-based privacy protection scheme for personal data. IEEE Trans. Reliab. 72(2), 586\u2013598 (2022)","journal-title":"IEEE Trans. Reliab."},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Liao, Z., Zhou, X., Liang, W., Li, K.C., Liu, Y., Zhao, Y.: Collaborative federated learning in mobile vehicle clouds for online ride-hailing passenger zones recommendation. IEEE Internet Things J. (2024)","DOI":"10.1109\/JIOT.2024.3420096"},{"key":"26_CR11","doi-asserted-by":"publisher","first-page":"4574","DOI":"10.1109\/TIFS.2021.3108434","volume":"16","author":"X Liu","year":"2021","unstructured":"Liu, X., Li, H., Xu, G., Chen, Z., Huang, X., Lu, R.: Privacy-enhanced federated learning against poisoning adversaries. IEEE Trans. Inf. Forensics Secur. 16, 4574\u20134588 (2021)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"26_CR12","doi-asserted-by":"publisher","first-page":"4277","DOI":"10.1109\/TSP.2022.3198176","volume":"70","author":"Y Liu","year":"2022","unstructured":"Liu, Y., et al.: FedBCD: a communication-efficient collaborative learning framework for distributed features. IEEE Trans. Signal Process. 70, 4277\u20134290 (2022)","journal-title":"IEEE Trans. Signal Process."},{"issue":"9","key":"26_CR13","doi-asserted-by":"publisher","first-page":"5880","DOI":"10.1002\/int.22818","volume":"37","author":"J Ma","year":"2022","unstructured":"Ma, J., Naas, S.A., Sigg, S., Lyu, X.: Privacy-preserving federated learning based on multi-key homomorphic encryption. Int. J. Intell. Syst. 37(9), 5880\u20135901 (2022)","journal-title":"Int. J. Intell. Syst."},{"key":"26_CR14","unstructured":"Melis, L., Song, C., De\u00a0Cristofaro, E., Shmatikov, V.: Inference attacks against collaborative learning. arXiv preprint arXiv:1805.04049 13 (2018)"},{"issue":"5","key":"26_CR15","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1109\/TIFS.2017.2787987","volume":"13","author":"LT Phong","year":"2018","unstructured":"Phong, L.T., Aono, Y., Hayashi, T., Wang, L., Moriai, S.: Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans. Inf. Forensics Secur. 13(5), 1333\u20131345 (2018). https:\/\/doi.org\/10.1109\/TIFS.2017.2787987","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"8","key":"26_CR16","doi-asserted-by":"publisher","first-page":"13437","DOI":"10.1109\/JIOT.2023.3339638","volume":"11","author":"L Shen","year":"2023","unstructured":"Shen, L., et al.: SPEFL: efficient security and privacy-enhanced federated learning against poisoning attacks. IEEE Internet Things J. 11(8), 13437\u201313451 (2023)","journal-title":"IEEE Internet Things J."},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310\u20131321 (2015)","DOI":"10.1145\/2810103.2813687"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3\u201318. IEEE (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"26_CR19","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","volume":"15","author":"K Wei","year":"2020","unstructured":"Wei, K., et al.: Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans. Inf. Forensics Secur. 15, 3454\u20133469 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"26_CR20","doi-asserted-by":"publisher","first-page":"4488","DOI":"10.1109\/TIFS.2023.3293417","volume":"18","author":"K Wei","year":"2023","unstructured":"Wei, K., et al.: Personalized federated learning with differential privacy and convergence guarantee. IEEE Trans. Inf. Forensics Secur. 18, 4488\u20134503 (2023)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"26_CR21","doi-asserted-by":"publisher","first-page":"4583","DOI":"10.1109\/TSP.2020.3012952","volume":"68","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Ling, Q., Chen, T., Giannakis, G.B.: Federated variance-reduced stochastic gradient descent with robustness to Byzantine attacks. IEEE Trans. Signal Process. 68, 4583\u20134596 (2020)","journal-title":"IEEE Trans. Signal Process."},{"key":"26_CR22","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. Forensics Secur. 15, 911\u2013926 (2019)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"2","key":"26_CR23","first-page":"1364","volume":"19","author":"G Xu","year":"2020","unstructured":"Xu, G., Li, H., Zhang, Y., Xu, S., Ning, J., Deng, R.H.: Privacy-preserving federated deep learning with irregular users. IEEE Trans. Dependable Secure Comput. 19(2), 1364\u20131381 (2020)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Xu, J., Li, H., Zeng, J., Hao, M.: Efficient and privacy-preserving federated learning with irregular users. In: ICC 2022-IEEE International Conference on Communications, pp. 534\u2013539. IEEE (2022)","DOI":"10.1109\/ICC45855.2022.9839208"},{"key":"26_CR25","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_CR26","doi-asserted-by":"crossref","unstructured":"Zhai, K., Ren, Q., Wang, J., Yan, C.: Byzantine-robust federated learning via credibility assessment on Non-IID data. arXiv preprint arXiv:2109.02396 (2021)","DOI":"10.3934\/mbe.2022078"},{"key":"26_CR27","unstructured":"Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., Liu, Y.: BatChcrypt: efficient homomorphic encryption for cross-silo federated learning. In: 2020 USENIX Annual Technical Conference (USENIX ATC 20), pp. 493\u2013506 (2020)"},{"key":"26_CR28","unstructured":"Zhang, C., Ekanut, S., Zhen, L., Li, Z.: Augmented multi-party computation against gradient leakage in federated learning. IEEE Trans. Big Data (2022)"},{"issue":"11","key":"26_CR29","doi-asserted-by":"publisher","first-page":"9768","DOI":"10.1109\/JIOT.2023.3235707","volume":"10","author":"S Zhang","year":"2023","unstructured":"Zhang, S., Hu, B., Liang, W., Li, K.C., Gupta, B.B.: A caching-based dual K-anonymous location privacy-preserving scheme for edge computing. IEEE Internet Things J. 10(11), 9768\u20139781 (2023)","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"26_CR30","doi-asserted-by":"publisher","first-page":"5745","DOI":"10.1109\/JIOT.2023.3308073","volume":"11","author":"S Zhang","year":"2023","unstructured":"Zhang, S., Hu, B., Liang, W., Li, K.C., Pathan, A.S.K.: A trajectory privacy-preserving scheme based on transition matrix and caching for IIoT. IEEE Internet Things J. 11(4), 5745\u20135756 (2023)","journal-title":"IEEE Internet Things J."},{"key":"26_CR31","doi-asserted-by":"publisher","first-page":"1486","DOI":"10.1109\/TIFS.2019.2939713","volume":"15","author":"L Zhao","year":"2019","unstructured":"Zhao, L., Wang, Q., Zou, Q., Zhang, Y., Chen, Y.: Privacy-preserving collaborative deep learning with unreliable participants. IEEE Trans. Inf. Forensics Secur. 15, 1486\u20131500 (2019)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"26_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, S., Li, K., Chen, Y., Yang, C., Liang, W., Zomaya, A.Y.: TrustBCFL: mitigating data bias in IoT through blockchain-enabled federated learning. IEEE Internet Things J. (2024)","DOI":"10.1109\/JIOT.2024.3379363"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Security and Privacy in Communication Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-23447-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T22:03:29Z","timestamp":1776981809000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-23447-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032234469","9783032234476"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-23447-6_26","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"24 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SecureComm","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Security and Privacy in Communication Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiangtan","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"securecomm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/securecomm.eai-conferences.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}