{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T10:53:51Z","timestamp":1792666431537,"version":"4.5.0"},"reference-count":110,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T00:00:00Z","timestamp":1656115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T00:00:00Z","timestamp":1656115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s10586-022-03644-w","type":"journal-article","created":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T08:02:51Z","timestamp":1656144171000},"page":"4075-4096","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Federated recommenders: methods, challenges and future"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2831-6483","authenticated-orcid":false,"given":"Zareen","family":"Alamgir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farwa K.","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saira","family":"Karim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,25]]},"reference":[{"key":"3644_CR1","unstructured":"Wu, L., Xiangnan, H., Wang, X., Zhang, K., Wang, M.: A survey on neural recommendation: from collaborative filtering to information-rich recommendation. http:\/\/arxiv.org\/abs\/2104.13030v22104.13030v2 (2021)"},{"key":"3644_CR2","doi-asserted-by":"publisher","first-page":"274","DOI":"10.3991\/ijet.v16i03.18851","volume":"16","author":"U Javed","year":"2021","unstructured":"Javed, U., Shaukat, K., Hameed, I.A., Iqbal, F., Alam, T.M., Luo, S.: A review of content-based and context-based recommendation systems. Int. J. Emerg. Technol. Learn. 16, 274\u2013306 (2021)","journal-title":"Int. J. Emerg. Technol. Learn."},{"key":"3644_CR3","doi-asserted-by":"publisher","first-page":"2098","DOI":"10.1109\/COMST.2021.3094993","volume":"23","author":"F Awaysheh","year":"2021","unstructured":"Awaysheh, F., Alazab, M., Garg, S., Niyato, D., Verikoukis, C.: Big data resource management and networks: taxonomy, survey, and future directions. IEEE Commun. Surv. Tutor. 23, 2098\u20132130 (2021)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"3644_CR4","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. http:\/\/arxiv.org\/abs\/1610.05492 (2016)"},{"key":"3644_CR5","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1109\/COMST.2021.3058573","volume":"23","author":"O Wahab","year":"2021","unstructured":"Wahab, O., Mourad, A., Otrok, H., Taleb, T.: Federated machine learning: survey, multi-level classification, desirable criteria and future directions in communication and networking systems. IEEE Commun. Surv. Tutor. 23, 1342\u20131397 (2021)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"3644_CR6","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1109\/COMST.2020.2970550","volume":"22","author":"X Wang","year":"2020","unstructured":"Wang, X., Han, Y., Leung, V., Niyato, D., Yan, X., Chen, X.: Convergence of edge computing and deep learning: a comprehensive survey. IEEE Commun. Surv. Tutor. 22, 869\u2013904 (2020)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"3644_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3339474","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10, 1\u201319 (2019)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"3644_CR8","unstructured":"Ammad-Ud-Din, M., Ivannikova, E., Khan, S., Oyomno, W., Fu, Q., Tan, K., Flanagan, A. Federated collaborative filtering for privacy-preserving personalized recommendation system. http:\/\/arxiv.org\/abs\/1901.09888 (2019)"},{"key":"3644_CR9","doi-asserted-by":"crossref","unstructured":"Flanagan, A., Oyomno, W., Grigorievskiy, A., Tan, K., Khan, S., Ammad-Ud-Din, M. Federated multi-view matrix factorization for personalized recommendations. http:\/\/arxiv.org\/abs\/2004.04256 (2020)","DOI":"10.1007\/978-3-030-67661-2_20"},{"key":"3644_CR10","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., Seth, K.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175\u20131191. Association for Computing Machinery, New York, NY, USA (2017)","DOI":"10.1145\/3133956.3133982"},{"key":"3644_CR11","unstructured":"Geyer, R.C., Klein, T., Nabi, M.: Differentially private federated learning: a client level perspective. http:\/\/arxiv.org\/abs\/1712.07557 (2017)"},{"key":"3644_CR12","unstructured":"Chen, F., Luo, M., Dong, Z., Li, Z., He, X.: Federated meta-learning with fast convergence and efficient communication. http:\/\/arxiv.org\/abs\/1802.07876 (2018)"},{"key":"3644_CR13","unstructured":"Smith, V., Chiang, C., Sanjabi, M., Talwalkar, A.: Federated multi-task learning. http:\/\/arxiv.org\/abs\/1705.10467 (2017)"},{"key":"3644_CR14","doi-asserted-by":"publisher","first-page":"450","DOI":"10.3390\/s22020450","volume":"22","author":"H Abreha","year":"2022","unstructured":"Abreha, H., Hayajneh, M., Serhani, M.: Federated learning in edge computing: a systematic survey. Sensors 22, 450 (2022)","journal-title":"Sensors"},{"key":"3644_CR15","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-981-13-1927-3_42","volume-title":"Smart Intelligent Computing And Applications","author":"S Reddy","year":"2019","unstructured":"Reddy, S., Nalluri, S., Kunisetti, S., Ashok, S., Venkatesh, B.: Content-based movie recommendation system using genre correlation. In: Satapathy, S.C., Bhateja, V., Das, S. (eds.) Smart Intelligent Computing And Applications, pp. 391\u2013397. Springer, Singapore (2019)"},{"key":"3644_CR16","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.eswa.2017.08.008","volume":"89","author":"J Son","year":"2017","unstructured":"Son, J., Kim, S.: Content-based filtering for recommendation systems using multiattribute networks. Expert Syst. Appl. 89, 404\u2013412 (2017)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"3644_CR17","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"key":"3644_CR18","doi-asserted-by":"publisher","first-page":"308","DOI":"10.26599\/BDMA.2018.9020008","volume":"1","author":"A Ramlatchan","year":"2018","unstructured":"Ramlatchan, A., Yang, M., Liu, Q., Li, M., Wang, J., Li, Y.: A survey of matrix completion methods for recommendation systems. Big Data Min. Anal. 1, 308\u2013323 (2018)","journal-title":"Big Data Min. Anal."},{"key":"3644_CR19","doi-asserted-by":"publisher","first-page":"21369","DOI":"10.1109\/ACCESS.2018.2819428","volume":"6","author":"C Lin","year":"2018","unstructured":"Lin, C., Wang, L., Tsai, K.: Hybrid real-time matrix factorization for implicit feedback recommendation systems. IEEE Access 6, 21369\u201321380 (2018)","journal-title":"IEEE Access"},{"key":"3644_CR20","doi-asserted-by":"crossref","unstructured":"Alfarhood, M., Cheng, J.: DeepHCF: a deep learning based hybrid collaborative filtering approach for recommendation systems. In: Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). pp. 89-96 (2018)","DOI":"10.1109\/ICMLA.2018.00021"},{"key":"3644_CR21","doi-asserted-by":"publisher","first-page":"90978","DOI":"10.1109\/ACCESS.2020.2994056","volume":"8","author":"S Patro","year":"2020","unstructured":"Patro, S., Mishra, B., Panda, S., Kumar, R., Long, H., Taniar, D., Priyadarshini, I.: A hybrid action-related K-nearest neighbour (HAR-KNN) approach for recommendation systems. IEEE Access 8, 90978\u201390991 (2020)","journal-title":"IEEE Access"},{"key":"3644_CR22","doi-asserted-by":"crossref","unstructured":"Zarzour, H., Al-Sharif, Z., Al-Ayyoub, M., Jararweh, Y. A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In: Proceedings of the 2018 9th International Conference On Information And Communication Systems (ICICS). pp. 102-106 (2018)","DOI":"10.1109\/IACS.2018.8355449"},{"key":"3644_CR23","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.ipm.2018.10.003","volume":"56","author":"P S\u00e1nchez","year":"2019","unstructured":"S\u00e1nchez, P., Bellog\u00edn, A.: Building user profiles based on sequences for content and collaborative filtering. Inf. Process. Manag. 56, 192\u2013211 (2019)","journal-title":"Inf. Process. Manag."},{"key":"3644_CR24","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-981-10-8968-8_12","volume-title":"Smart Innovations in Communication and Computational Sciences","author":"N Pereira","year":"2019","unstructured":"Pereira, N., Varma, S.: Financial planning recommendation system using content-based collaborative and demographic filtering. In: Tiwari, S., Trivedi, M.C., Mishra, K.K., Misra, A.K., Kumar, K.K. (eds.) Smart Innovations in Communication and Computational Sciences, pp. 141\u2013151. Springer, Singapore (2019)"},{"issue":"6","key":"3644_CR25","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.3233\/IDA-163209","volume":"21","author":"E \u00c7ano","year":"2017","unstructured":"\u00c7ano, E., Morisio, M.: Hybrid recommender systems: a systematic literature review. Intell. Data Anal. 21(6), 1487\u20131524 (2017)","journal-title":"Intell. Data Anal."},{"issue":"1","key":"3644_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3158369","volume":"52","author":"S Zhang","year":"2019","unstructured":"Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. (CSUR) 52(1), 1\u201338 (2019)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"3644_CR27","doi-asserted-by":"crossref","unstructured":"Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191\u2013198 (2016)","DOI":"10.1145\/2959100.2959190"},{"key":"3644_CR28","doi-asserted-by":"crossref","unstructured":"Okura, S., Tagami, Y., Ono, S., Tajima, A.: Embedding-based news recommendation for millions of users. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1933\u20131942 (2017)","DOI":"10.1145\/3097983.3098108"},{"key":"3644_CR29","doi-asserted-by":"crossref","unstructured":"Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7\u201310. Association for Computing Machinery, New York, NY, USA (2016)","DOI":"10.1145\/2988450.2988454"},{"key":"3644_CR30","doi-asserted-by":"crossref","unstructured":"Li, P., Wang, Z., Ren, Z., Bing, L., Lam, W.: Neural rating regression with abstractive tips generation for recommendation. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 345\u2013354 (2017)","DOI":"10.1145\/3077136.3080822"},{"key":"3644_CR31","doi-asserted-by":"publisher","unstructured":"Nguyen, H.T., Wistuba, M., Grabocka, J., Drumond, L.R., Schmidt-Thieme, L.: Personalized deep learning for tag recommendation. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 186\u2013197. Springer (2017). https:\/\/doi.org\/10.1007\/978-3-319-57454-7_15","DOI":"10.1007\/978-3-319-57454-7_15"},{"key":"3644_CR32","first-page":"27","volume":"34","author":"L Chen","year":"2020","unstructured":"Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M.: Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. Proc. AAAI Conf. Artif. Intell. 34, 27\u201334 (2020)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"3644_CR33","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.eswa.2016.09.013","volume":"67","author":"B Shams","year":"2017","unstructured":"Shams, B., Haratizadeh, S.: Graph-based collaborative ranking. Expert Syst. Appl. 67, 59\u201370 (2017). https:\/\/doi.org\/10.1016\/j.eswa.2016.09.013","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"3644_CR34","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1016\/j.ipm.2016.12.003","volume":"53","author":"C Musto","year":"2017","unstructured":"Musto, C., Basile, P., Lops, P., de Gemmis, M., Semeraro, G.: Introducing linked open data in graph-based recommender systems. Inf. Process. Manag. 53(2), 405\u2013435 (2017). https:\/\/doi.org\/10.1016\/j.ipm.2016.12.003","journal-title":"Inf. Process. Manag."},{"key":"3644_CR35","first-page":"276","volume":"47","author":"J Leng","year":"2017","unstructured":"Leng, J., Jiang, P.: Mining and matching relationships from interaction contexts in a social manufacturing paradigm. IEEE Trans. Syst. Man Cybern. 47, 276\u2013288 (2017)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"3644_CR36","unstructured":"Guo, Q., Zhuang, F., Qin, C., Zhu, H., Xie, X., Xiong, H., He, Q. A survey on knowledge graph-based recommender systems. In: Proceedings of the IEEE Transactions on Knowledge and Data Engineering, pp. 1\u20131 (2020)"},{"key":"3644_CR37","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1016\/j.ins.2021.05.064","volume":"574","author":"G Li","year":"2021","unstructured":"Li, G., Wang, G., Dong, J., Yeh, W., Li, K.: DLEA: a dynamic learning evolution algorithm for many-objective optimization. Inf. Sci. 574, 567\u2013589 (2021)","journal-title":"Inf. Sci."},{"key":"3644_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11831-021-09562-1","volume":"28","author":"W Li","year":"2021","unstructured":"Li, W., Gai, W., Gandomi, A.: A survey of learning-based intelligent optimization algorithms. Arch. Comput. Methods Eng. 28, 1\u201319 (2021)","journal-title":"Arch. Comput. Methods Eng."},{"key":"3644_CR39","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.comcom.2020.07.028","volume":"161","author":"M Etemadi","year":"2020","unstructured":"Etemadi, M., Ghobaei-Arani, M., Shahidinejad, A.: Resource provisioning for IoT services in the fog computing environment: an autonomic approach. Comput. Commun. 161, 109\u2013131 (2020)","journal-title":"Comput. Commun."},{"key":"3644_CR40","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/s10586-020-03107-0","volume":"24","author":"A Shahidinejad","year":"2021","unstructured":"Shahidinejad, A., Ghobaei-Arani, M., Masdari, M.: Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Clust. Comput. 24, 319\u2013342 (2021)","journal-title":"Clust. Comput."},{"key":"3644_CR41","doi-asserted-by":"crossref","unstructured":"McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS\u201907), vol. 7, pp. 94\u2013103. IEEE (2007)","DOI":"10.1109\/FOCS.2007.66"},{"key":"3644_CR42","unstructured":"Mugunthan, V., Polychroniadou, A., Byrd, D., Balch, T.H.: Smpai: secure multi-party computation for federated learning. Neural Information Processing. https:\/\/www.jpmorgan.com\/jpmpdf\/1320748217124.pdf (2019)"},{"key":"3644_CR43","doi-asserted-by":"crossref","unstructured":"Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., Zhou, Y.: A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, pp. 1\u201311 (2019)","DOI":"10.1145\/3338501.3357370"},{"key":"3644_CR44","unstructured":"Giacomelli, I., Jha, S., Joye, M., Page, C.D., Yoon, K.: Privacy-preserving ridge regression with only linearly-homomorphic encryption. Cryptology ePrint Archive, Report 2017\/979. https:\/\/eprint.iacr.org\/2017\/979 (2017)"},{"key":"3644_CR45","doi-asserted-by":"crossref","unstructured":"Anelli, V., Deldjoo, Y., Di Noia, T., Ferrara, A., Narducci, F. FedeRank: user controlled feedback with federated recommender systems. http:\/\/arxiv.org\/abs\/2012.11328 (2020)","DOI":"10.1007\/978-3-030-72113-8_3"},{"key":"3644_CR46","doi-asserted-by":"publisher","unstructured":"Anelli, V.W., Deldjoo, Y., Di\u00a0Noia, T., Ferrara, A., Narducci, F.: How to put users in control of their data in federated top-n recommendation with learning to rank. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing. SAC \u201921, pp. 1359\u20131362. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3412841.3442010","DOI":"10.1145\/3412841.3442010"},{"key":"3644_CR47","unstructured":"Chen, C., Zhang, J., Tung, A.K.H., Kankanhalli, M., Chen, G.: Robust federated recommendation system. http:\/\/arxiv.org\/abs\/2006.08259 (2020)"},{"key":"3644_CR48","doi-asserted-by":"crossref","unstructured":"Muhammad, K., Wang, Q., O\u2019Reilly-Morgan, D., Tragos, E., Smyth, B., Hurley, N., Geraci, J., Lawlor, A.: Fedfast: going beyond average for faster training of federated recommender systems. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1234\u20131242. Association for Computing Machinery, New York, NY, USA (2020)","DOI":"10.1145\/3394486.3403176"},{"key":"3644_CR49","doi-asserted-by":"crossref","unstructured":"Jalalirad, A., Scavuzzo, M., Capota, C., Sprague, M.: A simple and efficient federated recommender system. In: Proceedings of the 6th IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 53\u201358 (2019)","DOI":"10.1145\/3365109.3368788"},{"key":"3644_CR50","doi-asserted-by":"publisher","unstructured":"Lin, Y., Ren, P., Chen, Z., Ren, Z., Yu, D., Ma, J., Rijke, M.D., Cheng, X.: Meta matrix factorization for federated rating predictions. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 981\u2013990. Association for Computing Machinery, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3397271.3401081","DOI":"10.1145\/3397271.3401081"},{"key":"3644_CR51","doi-asserted-by":"crossref","unstructured":"Zhao, S., Bharati, R., Borcea, C., Chen, Y. Privacy-aware federated learning for page recommendation. In: Proceedings of the 2020 IEEE International Conference On Big Data (Big Data), pp. 1071\u20131080 (2020)","DOI":"10.1109\/BigData50022.2020.9377942"},{"key":"3644_CR52","doi-asserted-by":"publisher","first-page":"83340","DOI":"10.1109\/ACCESS.2021.3087406","volume":"9","author":"B Shi","year":"2021","unstructured":"Shi, B., Tragos, E.Z., Ozsoy, M.G., Dong, R., Hurley, N., Smyth, B., Lawlor, A.: Dares: an asynchronous distributed recommender system using deep reinforcement learning. IEEE Access 9, 83340\u201383354 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3087406","journal-title":"IEEE Access"},{"key":"3644_CR53","doi-asserted-by":"crossref","unstructured":"Zhou, P., Wang, K., Guo, L., Gong, S., Zheng, B.: A privacy-preserving distributed contextual federated online learning framework with big data support in social recommender systems. In: Proceedings of the IEEE Transactions on Knowledge and Data Engineering (2019)","DOI":"10.1109\/TKDE.2019.2936565"},{"key":"3644_CR54","unstructured":"Chai, D., Wang, L., Chen, K., Yang, Q.: Secure federated matrix factorization. http:\/\/arxiv.org\/abs\/1906.05108 (2019)"},{"key":"3644_CR55","first-page":"012055","volume":"1792","author":"X Wang","year":"2021","unstructured":"Wang, X., Zheng, X., Liang, X.: Charging station recommendation for electric vehicle based on federated learning. J. Phys. 1792, 012055 (2021)","journal-title":"J. Phys."},{"key":"3644_CR56","doi-asserted-by":"crossref","unstructured":"Wang, L.-E., Wang, Y., Bai, Y., Liu, P., Li, X.: POI recommendation with federated learning and privacy preserving in cross domain recommendation. In: Proceedings of the IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/INFOCOMWKSHPS51825.2021.9484510"},{"key":"3644_CR57","unstructured":"Ribero, M., Henderson, J., Williamson, S., Vikalo, H. Federating recommendations using differentially private prototypes. http:\/\/arxiv.org\/abs\/2003.00602 (2020)"},{"key":"3644_CR58","doi-asserted-by":"crossref","unstructured":"Qi, T., Wu, F., Wu, C., Huang, Y., Xie, X.: FedRec: privacy-preserving news recommendation with federated learning. http:\/\/arxiv.org\/abs\/2003.09592 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.128"},{"issue":"3","key":"3644_CR59","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1007\/s42045-020-00045-8","volume":"3","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Tian, Y., Yin, X., Hei, X.: A trusted recommendation scheme for privacy protection based on federated learning. CCF Trans. Netw. 3(3), 218\u2013228 (2020)","journal-title":"CCF Trans. Netw."},{"key":"3644_CR60","doi-asserted-by":"crossref","unstructured":"Qin, J., Liu, B., Qian, J.: A novel privacy-preserved recommender system framework based on federated learning. In: Proceedings of the 2021 The 4th International Conference On Software Engineering and Information Management. pp. 82-88 (2021)","DOI":"10.1145\/3451471.3451485"},{"key":"3644_CR61","doi-asserted-by":"publisher","unstructured":"Khan, F.K., Flanagan, A., Tan, K.E., Alamgir, Z., Ammad-Ud-Din, M.: A payload optimization method for federated recommender systems. In: Proceedings of the Fifteenth ACM Conference on Recommender Systems. RecSys \u201921. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3460231.3474257","DOI":"10.1145\/3460231.3474257"},{"key":"3644_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107700","volume":"111","author":"Y Du","year":"2021","unstructured":"Du, Y., Zhou, D., Xie, Y., Shi, J., Gong, M.: Federated matrix factorization for privacy-preserving recommender systems. Appl. Soft Comput. 111, 107700 (2021)","journal-title":"Appl. Soft Comput."},{"key":"3644_CR63","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1093\/comjnl\/bxab025","volume":"64","author":"W Ali","year":"2021","unstructured":"Ali, W., Kumar, R., Deng, Z., Wang, Y., Shao, J.: A federated learning approach for privacy protection in context-aware recommender systems. Comput. J. 64, 1016\u20131027 (2021)","journal-title":"Comput. J."},{"key":"3644_CR64","doi-asserted-by":"crossref","unstructured":"Hu, H., Dobbie, G., Salcic, Z., Liu, M., Zhang, J., Lyu, L., Zhang, X. Differentially private locality sensitive hashing based federated recommender system. Concurrency and Computation: Practice And Experience. pp. e6233 (2021)","DOI":"10.1002\/cpe.6233"},{"key":"3644_CR65","doi-asserted-by":"crossref","unstructured":"Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques, pp. 223\u2013238. Springer (1999)","DOI":"10.1007\/3-540-48910-X_16"},{"key":"3644_CR66","unstructured":"Gao, D., Tan, B., Ju, C., Zheng, V., Yang, Q. Privacy threats against federated matrix factorization. http:\/\/arxiv.org\/abs\/2007.01587 (2020)"},{"key":"3644_CR67","doi-asserted-by":"crossref","unstructured":"Kabbur, S., Ning, X., Karypis, G.: Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 659\u2013667 (2013)","DOI":"10.1145\/2487575.2487589"},{"key":"3644_CR68","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. http:\/\/arxiv.org\/abs\/1412.6980 (2014)"},{"key":"3644_CR69","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"3644_CR70","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, Vol. 70, pp. 1126\u20131135. JMLR.org (2017)"},{"key":"3644_CR71","unstructured":"Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. http:\/\/arxiv.org\/abs\/1803.02999 (2018)"},{"key":"3644_CR72","unstructured":"Lu, T., P\u00e1l, D., P\u00e1l, M.: Contextual multi-armed bandits. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 485\u2013492 (2010)"},{"key":"3644_CR73","doi-asserted-by":"crossref","unstructured":"Vyas, J., Das, D., Das, S.K.: Vehicular edge computing based driver recommendation system using federated learning. In: Proceedings of the 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 675\u2013683. IEEE (2020)","DOI":"10.1109\/MASS50613.2020.00087"},{"key":"3644_CR74","doi-asserted-by":"crossref","unstructured":"Antunes, R., Costa, C., K\u00fcderle, A., Yari, I., Eskofier, B.: Federated learning for healthcare: systematic review and architecture proposal. ACM Trans. Intell. Syst. Technol. (TIST) (2022)","DOI":"10.1145\/3501813"},{"key":"3644_CR75","unstructured":"Caldas, S., Duddu, S.M.K., Wu, P., Li, T., Kone\u010dn\u1ef3, J., McMahan, H.B., Smith, V., Talwalkar, A.: LEAF: a benchmark for federated settings. http:\/\/arxiv.org\/abs\/1812.01097 (2019)"},{"issue":"4","key":"3644_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2827872","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 1\u201319 (2015)","journal-title":"ACM Trans. Interact. Intell. Syst."},{"issue":"2","key":"3644_CR77","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1145\/2812802","volume":"59","author":"B Thomee","year":"2016","unstructured":"Thomee, B., Shamma, D.A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., Li, L.-J.: Yfcc100m: the new data in multimedia research. Commun. ACM 59(2), 64\u201373 (2016)","journal-title":"Commun. ACM"},{"key":"3644_CR78","doi-asserted-by":"crossref","unstructured":"Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22\u201332. ACM, (2005)","DOI":"10.1145\/1060745.1060754"},{"key":"3644_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.178","volume":"5","author":"TJ Pollard","year":"2018","unstructured":"Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Sci. Data 5, 1\u201313 (2018)","journal-title":"Sci. Data"},{"key":"3644_CR80","unstructured":"Yelp: yelp open dataset. https:\/\/www.yelp.com\/dataset"},{"key":"3644_CR81","unstructured":"TripAdvisor: tripadvisor dataset. http:\/\/times.cs.uiuc.edu\/wang296\/Data\/"},{"key":"3644_CR82","doi-asserted-by":"crossref","unstructured":"Wu, F., Qiao, Y., Chen, J.-H., Wu, C., Qi, T., Lian, J., Liu, D., Xie, X., Gao, J., Wu, W., et al.: Mind: a large-scale dataset for news recommendation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3597\u20133606 (2020)","DOI":"10.18653\/v1\/2020.acl-main.331"},{"key":"3644_CR83","doi-asserted-by":"publisher","unstructured":"Cantador, I., Brusilovsky, P., Kuflik, T.: Second workshop on information heterogeneity and fusion in recommender systems (hetrec2011). In: RecSys\u201911 - Proceedings of the 5th ACM Conference on Recommender Systems, pp. 387\u2013388 (2011). https:\/\/doi.org\/10.1145\/2043932.2044016","DOI":"10.1145\/2043932.2044016"},{"key":"3644_CR84","unstructured":"Wang, H., Chen, B., Li, W.-J.: Collaborative topic regression with social regularization for tag recommendation. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2719\u20132725. AAAI Press (2013)"},{"key":"3644_CR85","unstructured":"Authors, T.T.: TensorFlow federated. https:\/\/www.tensorflow.org\/federated (2019)"},{"key":"3644_CR86","unstructured":"Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., Passerat-Palmbach, J.: A generic framework for privacy preserving deep learning (2018)"},{"key":"3644_CR87","unstructured":"Beutel, D.J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., Lane, N.D.: Flower: a friendly federated learning research framework (2020)"},{"key":"3644_CR88","unstructured":"IBM: IBM federated learning. https:\/\/github.com\/IBM\/federated-learning-lib.git (2020)"},{"key":"3644_CR89","unstructured":"Authors, T.F.: Federated AI technology enabler. https:\/\/www.fedai.org\/ (2019)"},{"key":"3644_CR90","unstructured":"Authors, T.C.T.F.: NVIDIA Clara. https:\/\/developer.nvidia.com\/clara (2019)"},{"key":"3644_CR91","unstructured":"Authors, T.P.: PaddleFL. https:\/\/github.com\/PaddlePaddle\/PaddleFL (2019)"},{"key":"3644_CR92","unstructured":"Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning. In: Chiappa, S., Calandra, R. (eds.) International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 108, pp. 2938\u20132948. PMLR (2020)"},{"key":"3644_CR93","doi-asserted-by":"publisher","unstructured":"Chirita, P.-A., Nejdl, W., Zamfir, C.: Preventing shilling attacks in online recommender systems. In: Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management, pp. 67\u201374. Association for Computing Machinery, New York, NY, USA (2005). https:\/\/doi.org\/10.1145\/1097047.1097061","DOI":"10.1145\/1097047.1097061"},{"key":"3644_CR94","doi-asserted-by":"publisher","first-page":"e0196533","DOI":"10.1371\/journal.pone.0196533","volume":"13","author":"W Zhou","year":"2018","unstructured":"Zhou, W., Wen, J., Qu, Q., Zeng, J., Cheng, T.: Shilling attack detection for recommender systems based on credibility of group users and rating time series. PLoS ONE 13, e0196533 (2018)","journal-title":"PLoS ONE"},{"key":"3644_CR95","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.knosys.2017.07.023","volume":"143","author":"J Leng","year":"2018","unstructured":"Leng, J., Chen, Q., Mao, N., Jiang, P.: Combining granular computing technique with deep learning for service planning under social manufacturing contexts. Knowl.-Based Syst. 143, 295\u2013306 (2018)","journal-title":"Knowl.-Based Syst."},{"key":"3644_CR96","first-page":"1618","volume":"49","author":"R Khorsand","year":"2019","unstructured":"Khorsand, R., Ghobaei-Arani, M., Ramezanpour, M.: A self-learning fuzzy approach for proactive resource provisioning in cloud environment. Software 49, 1618\u20131642 (2019)","journal-title":"Software"},{"key":"3644_CR97","unstructured":"Colin, I., Bellet, A., Salmon, J., Cl\u00e9men\u00e7on, S.: Gossip dual averaging for decentralized optimization of pairwise functions. http:\/\/arxiv.org\/abs\/1606.02421 (2016)"},{"key":"3644_CR98","unstructured":"Bellet, A., Guerraoui, R., Taziki, M., Tommasi, M.: Personalized and private peer-to-peer machine learning. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 473\u2013481 (2018)"},{"key":"3644_CR99","doi-asserted-by":"crossref","unstructured":"Elgabli, A., Park, J., Bedi, A.S., Bennis, M., Aggarwal, V.: Gadmm: Fast and communication efficient framework for distributed machine learning. http:\/\/arxiv.org\/abs\/1909.00047 (2019)","DOI":"10.1109\/CISS48834.2020.1570627384"},{"key":"3644_CR100","doi-asserted-by":"crossref","unstructured":"Heged\u0171s, I., Danner, G., Jelasity, M.: Decentralized recommendation based on matrix factorization: a comparison of gossip and federated learning. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 317\u2013332. Springer (2019)","DOI":"10.1007\/978-3-030-43823-4_27"},{"key":"3644_CR101","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/978-981-13-5953-8_44","volume-title":"Emerging Research in Computing, Information, Communication and Applications","author":"A Dhruv","year":"2019","unstructured":"Dhruv, A., Kamath, A., Powar, A., Gaikwad, K.: Artist recommendation system using hybrid method: a novel approach. In: Shetty, N.R., Prasad, N.H., Nalini, N. (eds.) Emerging Research in Computing, Information, Communication and Applications, pp. 527\u2013542. Springer, Singapore (2019)"},{"issue":"1","key":"3644_CR102","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10660-016-9240-9","volume":"17","author":"Y Ma","year":"2017","unstructured":"Ma, Y., Chen, G., Wei, Q.: Finding users preferences from large-scale online reviews for personalized recommendation. Electron. Commer. Res. 17(1), 3\u201329 (2017)","journal-title":"Electron. Commer. Res."},{"key":"3644_CR103","doi-asserted-by":"crossref","unstructured":"Chen, J., Zhang, C., Niu, Z.: Identifying helpful online reviews with word embedding features. In: Proceedings of the International Conference on Knowledge Science, Engineering and Management, pp. 123\u2013133. Springer (2016)","DOI":"10.1007\/978-3-319-47650-6_10"},{"issue":"2","key":"3644_CR104","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1016\/j.tele.2018.01.001","volume":"35","author":"P-J Lee","year":"2018","unstructured":"Lee, P.-J., Hu, Y.-H., Lu, K.-T.: Assessing the helpfulness of online hotel reviews: a classification-based approach. Telemat. Inform. 35(2), 436\u2013445 (2018)","journal-title":"Telemat. Inform."},{"key":"3644_CR105","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Ding, Y., Zhu, L., Kankanhalli, M.: Aspect-aware latent factor model: rating prediction with ratings and reviews. http:\/\/arxiv.org\/abs\/1802.07938 (2018)","DOI":"10.1145\/3178876.3186145"},{"key":"3644_CR106","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105675","volume":"195","author":"W Li","year":"2020","unstructured":"Li, W., Wang, G., Alavi, A.: Learning-based elephant herding optimization algorithm for solving numerical optimization problems. Knowl.-Based Syst. 195, 105675 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"3644_CR107","doi-asserted-by":"crossref","unstructured":"Li, W., Wang, G. Elephant herding optimization using dynamic topology and biogeography-based optimization based on learning for numerical optimization. Engineering With Computers. pp. 1-29 (2021)","DOI":"10.1007\/s00366-021-01293-y"},{"key":"3644_CR108","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-018-3699-3","volume":"32","author":"J Li","year":"2019","unstructured":"Li, J., Li, Y., Tian, S., Xia, J.: An improved cuckoo search algorithm with self-adaptive knowledge learning. Neural Comput. Appl. 32, 1\u201331 (2019)","journal-title":"Neural Comput. Appl."},{"key":"3644_CR109","unstructured":"Leng, J., Zhou, M., Zhao, J., Huang, Y., Bian, Y. Blockchain security: a survey of techniques and research directions. In Proceedings of the IEEE Transactions on Services Computing. pp. 1\u20131 (2020)"},{"key":"3644_CR110","doi-asserted-by":"crossref","unstructured":"Li, C., Yuan, Y., Wang, F. Blockchain-enabled federated learning: a survey. In: Proceedings of the 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), pp. 286\u2013289 (2021)","DOI":"10.1109\/DTPI52967.2021.9540163"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-022-03644-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-022-03644-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-022-03644-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T19:22:50Z","timestamp":1666898570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-022-03644-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,25]]},"references-count":110,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["3644"],"URL":"https:\/\/doi.org\/10.1007\/s10586-022-03644-w","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,25]]},"assertion":[{"value":"22 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declared that they have no conflict of interest to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The manuscript \u201cFederated Recommenders: Methods, Challenges and Future\u201d meets all the ethical requirements led by the Journal. This submission is guaranteed with the confirmation that the above-mentioned manuscript has not been published, accepted for publication elsewhere, or under editorial review for publication elsewhere. Furthermore, the work is not plagiarized and includes proper references wherever required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}