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Inf. Syst."],"published-print":{"date-parts":[[2023,4,30]]},"abstract":"<jats:p>\n            The generation of large amounts of personal data provides data centers with sufficient resources to mine idiosyncrasy from private records. User modeling has long been a fundamental task with the goal of capturing the latent characteristics of users from their behaviors. However, centralized user modeling on collected data has raised concerns about the risk of data misuse and privacy leakage. As a result, federated user modeling has come into favor, since it expects to provide secure multi-client collaboration for user modeling through federated learning. Unfortunately, to the best of our knowledge, existing federated learning methods that ignore the inconsistency among clients cannot be applied directly to practical user modeling scenarios, and moreover, they meet the following critical challenges: 1)\n            <jats:italic>Statistical heterogeneity<\/jats:italic>\n            . The distributions of user data in different clients are not always independently identically distributed (IID), which leads to unique clients with needful personalized information; 2)\n            <jats:italic>Privacy heterogeneity<\/jats:italic>\n            . User data contains both public and private information, which have different levels of privacy, indicating that we should balance different information shared and protected; 3)\n            <jats:italic>Model heterogeneity<\/jats:italic>\n            . The local user models trained with client records are heterogeneous, and thus require a flexible aggregation in the server; 4)\n            <jats:italic>Quality heterogeneity<\/jats:italic>\n            . Low-quality information from inconsistent clients poisons the reliability of user models and offsets the benefit from high-quality ones, meaning that we should augment the high-quality information during the process. To address the challenges, in this paper, we first propose a novel client-server architecture framework, namely Hierarchical Personalized Federated Learning (HPFL), with a primary goal of serving federated learning for user modeling in inconsistent clients. More specifically, the client train and deliver the local user model via the hierarchical components containing hierarchical information from privacy heterogeneity to join collaboration in federated learning. Moreover, the client updates the personalized user model with a fine-grained personalized update strategy for statistical heterogeneity. Correspondingly, the server flexibly aggregates hierarchical components from heterogeneous user models in the case of privacy and model heterogeneity with a differentiated component aggregation strategy. In order to augment high-quality information and generate high-quality user models, we expand HPFL to the Augmented-HPFL (AHPFL) framework by incorporating the augmented mechanisms, which filters out low-quality information such as noise, sparse information and redundant information. Specially, we construct two implementations of AHPFL, i.e., AHPFL-SVD and AHPFL-AE, where the augmented mechanisms follow SVD (singular value decomposition) and AE (autoencoder), respectively. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of both HPFL and AHPFL frameworks.\n          <\/jats:p>","DOI":"10.1145\/3560485","type":"journal-article","created":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T13:46:44Z","timestamp":1675950404000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":50,"title":["Federated User Modeling from Hierarchical Information"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6956-5550","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"first","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9957-5733","authenticated-orcid":false,"given":"Jinze","family":"Wu","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1661-0420","authenticated-orcid":false,"given":"Zhenya","family":"Huang","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9921-2078","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5713-3531","authenticated-orcid":false,"given":"Yuting","family":"Ning","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7464-0041","authenticated-orcid":false,"given":"Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2149-0670","authenticated-orcid":false,"given":"Jinfeng","family":"Yi","sequence":"additional","affiliation":[{"name":"JD AI Research, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1062-9526","authenticated-orcid":false,"given":"Bowen","family":"Zhou","sequence":"additional","affiliation":[{"name":"JD AI Research, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,4,3]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2006.881199"},{"key":"e_1_3_2_3_2","article-title":"Element level differential privacy: The right granularity of privacy","author":"Asi Hilal","year":"2019","unstructured":"Hilal Asi, John Duchi, and Omid Javidbakht. 2019. Element level differential privacy: The right granularity of privacy. arXiv preprint arXiv:1912.04042 (2019).","journal-title":"arXiv preprint arXiv:1912.04042"},{"key":"e_1_3_2_4_2","first-page":"634","volume-title":"International Conference on Machine Learning","author":"Bhagoji Arjun Nitin","year":"2019","unstructured":"Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. 2019. Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning. PMLR, 634\u2013643."},{"key":"e_1_3_2_5_2","article-title":"Augmentor: An image augmentation library for machine learning","author":"Bloice Marcus D","year":"2017","unstructured":"Marcus D Bloice, Christof Stocker, and Andreas Holzinger. 2017. Augmentor: An image augmentation library for machine learning. arXiv preprint arXiv:1708.04680 (2017).","journal-title":"arXiv preprint arXiv:1708.04680"},{"key":"e_1_3_2_6_2","first-page":"177","volume-title":"Proceedings of COMPSTAT\u20192010","author":"Bottou L\u00e9on","year":"2010","unstructured":"L\u00e9on Bottou. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT\u20192010. Springer, 177\u2013186."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.01.007"},{"key":"e_1_3_2_8_2","volume-title":"Data Protection: A Practical Guide to UK and EU Law","author":"Carey Peter","year":"2018","unstructured":"Peter Carey. 2018. Data Protection: A Practical Guide to UK and EU Law. 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IEEE Transactions on Neural Networks and Learning Systems 31, 10 (2019), 4229\u20134238.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_14_2","article-title":"Secureboost: A lossless federated learning framework","author":"Cheng Kewei","year":"2019","unstructured":"Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang. 2019. Secureboost: A lossless federated learning framework. arXiv preprint arXiv:1901.08755 (2019).","journal-title":"arXiv preprint arXiv:1901.08755"},{"key":"e_1_3_2_15_2","first-page":"3571","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Ding Bolin","year":"2017","unstructured":"Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. 2017. Collecting telemetry data privately. In Advances in Neural Information Processing Systems (NeurIPS). 3571\u20133580."},{"issue":"1","key":"e_1_3_2_16_2","first-page":"63","article-title":"Coefficient of determination","volume":"31","author":"Dodge Yadolah","year":"2006","unstructured":"Yadolah Dodge. 2006. Coefficient of determination. Alphascript Publishing 31, 1 (2006), 63\u201364.","journal-title":"Alphascript Publishing"},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/VISUAL.1994.346316","volume-title":"Proceedings Visualization\u201994","author":"Domik Gitta O","year":"1994","unstructured":"Gitta O Domik and Bernd Gutkauf. 1994. User modeling for adaptive visualization systems. In Proceedings Visualization\u201994. 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In Proceedings of the 24th International Conference on World Wide Web (WWW). 278\u2013288."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660348"},{"key":"e_1_3_2_22_2","article-title":"Federated multi-view matrix factorization for personalized recommendations","author":"Flanagan Adrian","year":"2020","unstructured":"Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A Khan, and Muhammad Ammad-Ud-Din. 2020. Federated multi-view matrix factorization for personalized recommendations. arXiv preprint arXiv:2004.04256 (2020).","journal-title":"arXiv preprint arXiv:2004.04256"},{"key":"e_1_3_2_23_2","first-page":"129","volume-title":"Proceedings of Graphics Interface 2005","author":"Fogarty James","year":"2005","unstructured":"James Fogarty, Ryan S Baker, and Scott E Hudson. 2005. Case studies in the use of ROC curve analysis for sensor-based estimates in human computer interaction. In Proceedings of Graphics Interface 2005. 129\u2013136."},{"key":"e_1_3_2_24_2","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer Robin C","year":"2017","unstructured":"Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 (2017).","journal-title":"arXiv preprint arXiv:1712.07557"},{"key":"e_1_3_2_25_2","article-title":"An efficient framework for clustered federated learning","author":"Ghosh Avishek","year":"2020","unstructured":"Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran. 2020. An efficient framework for clustered federated learning. arXiv preprint arXiv:2006.04088 (2020).","journal-title":"arXiv preprint arXiv:2006.04088"},{"key":"e_1_3_2_26_2","first-page":"249","volume-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. 249\u2013256."},{"key":"e_1_3_2_27_2","article-title":"One-shot federated learning","author":"Guha Neel","year":"2019","unstructured":"Neel Guha, Ameet Talwalkar, and Virginia Smith. 2019. One-shot federated learning. arXiv preprint arXiv:1902.11175 (2019).","journal-title":"arXiv preprint arXiv:1902.11175"},{"key":"e_1_3_2_28_2","first-page":"3973","volume-title":"International Conference on Machine Learning","author":"Hamer Jenny","year":"2020","unstructured":"Jenny Hamer, Mehryar Mohri, and Ananda Theertha Suresh. 2020. Fedboost: A communication-efficient algorithm for federated learning. In International Conference on Machine Learning. PMLR, 3973\u20133983."},{"key":"e_1_3_2_29_2","article-title":"Federated learning of a mixture of global and local models","author":"Hanzely Filip","year":"2020","unstructured":"Filip Hanzely and Peter Richt\u00e1rik. 2020. Federated learning of a mixture of global and local models. arXiv preprint arXiv:2002.05516 (2020).","journal-title":"arXiv preprint arXiv:2002.05516"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806504"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_32_2","article-title":"Federated learning of user authentication models","author":"Hosseini Hossein","year":"2020","unstructured":"Hossein Hosseini, Sungrack Yun, Hyunsin Park, Christos Louizos, Joseph Soriaga, and Max Welling. 2020. Federated learning of user authentication models. arXiv preprint arXiv:2007.04618 (2020).","journal-title":"arXiv preprint arXiv:2007.04618"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0230706"},{"key":"e_1_3_2_34_2","first-page":"1","article-title":"DP-FL: A novel differentially private federated learning framework for the unbalanced data","author":"Huang Xixi","year":"2020","unstructured":"Xixi Huang, Ye Ding, Zoe L Jiang, Shuhan Qi, Xuan Wang, and Qing Liao. 2020. DP-FL: A novel differentially private federated learning framework for the unbalanced data. World Wide Web (2020), 1\u201317.","journal-title":"World Wide Web"},{"key":"e_1_3_2_35_2","first-page":"7865","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Huang Yutao","year":"2021","unstructured":"Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, and Yong Zhang. 2021. Personalized cross-silo federated learning on non-iid data. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 7865\u20137873."},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10740"},{"key":"e_1_3_2_37_2","article-title":"Ekt: Exercise-aware knowledge tracing for student performance prediction","author":"Huang Zhenya","year":"2019","unstructured":"Zhenya Huang, Yu Yin, Enhong Chen, Hui Xiong, Yu Su, Guoping Hu, et\u00a0al. 2019. Ekt: Exercise-aware knowledge tracing for student performance prediction. IEEE Transactions on Knowledge and Data Engineering (2019).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_38_2","first-page":"arXiv\u20132006","article-title":"Federated semi-supervised learning with inter-client consistency","author":"Jeong Wonyong","year":"2020","unstructured":"Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang. 2020. Federated semi-supervised learning with inter-client consistency. arXiv E-prints (2020), arXiv\u20132006.","journal-title":"arXiv E-prints"},{"key":"e_1_3_2_39_2","first-page":"1","volume-title":"2019 International Joint Conference on Neural Networks (IJCNN)","author":"Ji Shaoxiong","year":"2019","unstructured":"Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, and Zi Huang. 2019. Learning private neural language modeling with attentive aggregation. In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 1\u20138."},{"key":"e_1_3_2_40_2","article-title":"Emerging trends in federated learning: From model fusion to federated x learning","author":"Ji Shaoxiong","year":"2021","unstructured":"Shaoxiong Ji, Teemu Saravirta, Shirui Pan, Guodong Long, and Anwar Walid. 2021. Emerging trends in federated learning: From model fusion to federated x learning. arXiv preprint arXiv:2102.12920 (2021).","journal-title":"arXiv preprint arXiv:2102.12920"},{"issue":"1","key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3418283","article-title":"Industrial federated topic modeling","volume":"12","author":"Jiang Di","year":"2021","unstructured":"Di Jiang, Yongxin Tong, Yuanfeng Song, Xueyang Wu, Weiwei Zhao, Jinhua Peng, Rongzhong Lian, Qian Xu, and Qiang Yang. 2021. Industrial federated topic modeling. ACM Transactions on Intelligent Systems and Technology (TIST) 12, 1 (2021), 1\u201322.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_42_2","article-title":"Quantifying the performance of federated transfer learning","author":"Jing Qinghe","year":"2019","unstructured":"Qinghe Jing, Weiyan Wang, Junxue Zhang, Han Tian, and Kai Chen. 2019. Quantifying the performance of federated transfer learning. ArXiv abs\/1912.12795 (2019).","journal-title":"ArXiv abs\/1912.12795"},{"issue":"12","key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.4249\/scholarpedia.2088","article-title":"Signal-to-noise ratio","volume":"1","author":"Johnson Don H","year":"2006","unstructured":"Don H Johnson. 2006. Signal-to-noise ratio. Scholarpedia 1, 12 (2006), 2088.","journal-title":"Scholarpedia"},{"key":"e_1_3_2_44_2","article-title":"Advances and open problems in federated learning","author":"Kairouz Peter","year":"2019","unstructured":"Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et\u00a0al. 2019. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019).","journal-title":"arXiv preprint arXiv:1912.04977"},{"key":"e_1_3_2_45_2","volume-title":"Sixteenth Annual Conference of the International Speech Communication Association","author":"Ko Tom","year":"2015","unstructured":"Tom Ko, Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur. 2015. Audio augmentation for speech recognition. In Sixteenth Annual Conference of the International Speech Communication Association."},{"key":"e_1_3_2_46_2","volume-title":"Test Equating: Methods and Practices","author":"Kolen Michael J","year":"2013","unstructured":"Michael J Kolen and Robert L Brennan. 2013. Test Equating: Methods and Practices. Springer Science & Business Media."},{"key":"e_1_3_2_47_2","article-title":"Fedmd: Heterogenous federated learning via model distillation","author":"Li Daliang","year":"2019","unstructured":"Daliang Li and Junpu Wang. 2019. Fedmd: Heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581 (2019).","journal-title":"arXiv preprint arXiv:1910.03581"},{"key":"e_1_3_2_48_2","article-title":"Federated optimization in heterogeneous networks","author":"Li Tian","year":"2018","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2018. Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 (2018).","journal-title":"arXiv preprint arXiv:1812.06127"},{"key":"e_1_3_2_49_2","article-title":"On the convergence of fedavg on non-iid data","author":"Li Xiang","year":"2019","unstructured":"Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2019. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189 (2019).","journal-title":"arXiv preprint arXiv:1907.02189"},{"issue":"4","key":"e_1_3_2_50_2","doi-asserted-by":"crossref","first-page":"4555","DOI":"10.1109\/LRA.2019.2931179","article-title":"Lifelong federated reinforcement learning: A learning architecture for navigation in cloud robotic systems","volume":"4","author":"Liu Boyi","year":"2019","unstructured":"Boyi Liu, Lujia Wang, and Ming Liu. 2019. Lifelong federated reinforcement learning: A learning architecture for navigation in cloud robotic systems. IEEE Robotics and Automation Letters 4, 4 (2019), 4555\u20134562.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1109\/ICDM.2011.118","volume-title":"2011 IEEE 11th International Conference on Data Mining","author":"Liu Qi","year":"2011","unstructured":"Qi Liu, Yong Ge, Zhongmou Li, Enhong Chen, and Hui Xiong. 2011. Personalized travel package recommendation. In 2011 IEEE 11th International Conference on Data Mining. IEEE, 407\u2013416."},{"issue":"4","key":"e_1_3_2_52_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3168361","article-title":"Fuzzy cognitive diagnosis for modelling examinee performance","volume":"9","author":"Liu Qi","year":"2018","unstructured":"Qi Liu, Runze Wu, Enhong Chen, Guandong Xu, Yu Su, Zhigang Chen, and Guoping Hu. 2018. Fuzzy cognitive diagnosis for modelling examinee performance. ACM Transactions on Intelligent Systems and Technology (TIST) 9, 4 (2018), 1\u201326.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462825"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988525"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.2992755"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313751"},{"key":"e_1_3_2_57_2","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten Laurens van der","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, Nov (2008), 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_58_2","first-page":"281","volume-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability","volume":"1","author":"MacQueen James","year":"1967","unstructured":"James MacQueen et\u00a0al. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1. Oakland, CA, USA, 281\u2013297."},{"key":"e_1_3_2_59_2","article-title":"Three approaches for personalization with applications to federated learning","author":"Mansour Yishay","year":"2020","unstructured":"Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh. 2020. Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619 (2020).","journal-title":"arXiv preprint arXiv:2002.10619"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3223045"},{"key":"e_1_3_2_61_2","first-page":"1273","volume-title":"Artificial Intelligence and Statistics","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics. 1273\u20131282."},{"key":"e_1_3_2_62_2","article-title":"Learning differentially private recurrent language models","author":"McMahan H Brendan","year":"2017","unstructured":"H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017. Learning differentially private recurrent language models. arXiv preprint arXiv:1710.06963 (2017).","journal-title":"arXiv preprint arXiv:1710.06963"},{"key":"e_1_3_2_63_2","article-title":"Cross-node federated graph neural network for spatio-temporal data modeling","author":"Meng Chuizheng","year":"2021","unstructured":"Chuizheng Meng, Sirisha Rambhatla, and Yan Liu. 2021. Cross-node federated graph neural network for spatio-temporal data modeling. arXiv preprint arXiv:2106.05223 (2021).","journal-title":"arXiv preprint arXiv:2106.05223"},{"key":"e_1_3_2_64_2","article-title":"Agnostic federated learning","author":"Mohri Mehryar","year":"2019","unstructured":"Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh. 2019. Agnostic federated learning. arXiv preprint arXiv:1902.00146 (2019).","journal-title":"arXiv preprint arXiv:1902.00146"},{"key":"e_1_3_2_65_2","article-title":"Robust federated learning through representation matching and adaptive hyper-parameters","author":"Mostafa Hesham","year":"2019","unstructured":"Hesham Mostafa. 2019. Robust federated learning through representation matching and adaptive hyper-parameters. arXiv preprint arXiv:1912.13075 (2019).","journal-title":"arXiv preprint arXiv:1912.13075"},{"key":"e_1_3_2_66_2","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1145\/3394486.3403176","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Muhammad Khalil","year":"2020","unstructured":"Khalil Muhammad, Qinqin Wang, Diarmuid O\u2019Reilly-Morgan, Elias Tragos, Barry Smyth, Neil Hurley, James Geraci, and Aonghus Lawlor. 2020. 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. 1234\u20131242."},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-014-0007-7"},{"key":"e_1_3_2_68_2","article-title":"Private federated learning with domain adaptation","author":"Peterson Daniel","year":"2019","unstructured":"Daniel Peterson, Pallika Kanani, and Virendra J Marathe. 2019. Private federated learning with domain adaptation. arXiv preprint arXiv:1912.06733 (2019).","journal-title":"arXiv preprint arXiv:1912.06733"},{"issue":"1","key":"e_1_3_2_69_2","first-page":"1","article-title":"Smartphone ownership and internet usage continues to climb in emerging economies","volume":"22","author":"Poushter Jacob","year":"2016","unstructured":"Jacob Poushter et\u00a0al. 2016. Smartphone ownership and internet usage continues to climb in emerging economies. Pew Research Center 22, 1 (2016), 1\u201344.","journal-title":"Pew Research Center"},{"key":"e_1_3_2_70_2","article-title":"Privacy-preserving news recommendation model training via federated learning","author":"Qi Tao","year":"2020","unstructured":"Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, and Xing Xie. 2020. Privacy-preserving news recommendation model training via federated learning. arXiv preprint arXiv:2003.09592 (2020).","journal-title":"arXiv preprint arXiv:2003.09592"},{"key":"e_1_3_2_71_2","article-title":"Privacy preserving text recognition with gradient-boosting for federated learning","author":"Ren Hanchi","year":"2020","unstructured":"Hanchi Ren, Jingjing Deng, and Xianghua Xie. 2020. Privacy preserving text recognition with gradient-boosting for federated learning. arXiv preprint arXiv:2007.07296 (2020).","journal-title":"arXiv preprint arXiv:2007.07296"},{"issue":"1","key":"e_1_3_2_72_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13173-017-0054-y","article-title":"Ranking lawyers using a social network induced by legal cases","volume":"23","author":"Ribeiro Leonardo Filipe Rodrigues","year":"2017","unstructured":"Leonardo Filipe Rodrigues Ribeiro and Daniel Ratton Figueiredo. 2017. Ranking lawyers using a social network induced by legal cases. Journal of the Brazilian Computer Society 23, 1 (2017), 6.","journal-title":"Journal of the Brazilian Computer Society"},{"key":"e_1_3_2_73_2","first-page":"8253","volume-title":"International Conference on Machine Learning","author":"Rothchild Daniel","year":"2020","unstructured":"Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora. 2020. Fetchsgd: Communication-efficient federated learning with sketching. In International Conference on Machine Learning. PMLR, 8253\u20138265."},{"key":"e_1_3_2_74_2","article-title":"Robust and communication-efficient federated learning from non-iid data","author":"Sattler Felix","year":"2019","unstructured":"Felix Sattler, Simon Wiedemann, Klaus-Robert M\u00fcller, and Wojciech Samek. 2019. Robust and communication-efficient federated learning from non-iid data. IEEE Transactions on Neural Networks and Learning Systems (2019).","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_75_2","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1145\/1099554.1099747","volume-title":"Proceedings of the 14th ACM International Conference on Information and Knowledge Management","author":"Shen Xuehua","year":"2005","unstructured":"Xuehua Shen, Bin Tan, and ChengXiang Zhai. 2005. Implicit user modeling for personalized search. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management. 824\u2013831."},{"issue":"4","key":"e_1_3_2_76_2","first-page":"1413","article-title":"Deep collaborative filtering with multi-aspect information in heterogeneous networks","volume":"33","author":"Shi Chuan","year":"2019","unstructured":"Chuan Shi, Xiaotian Han, Li Song, Xiao Wang, Senzhang Wang, Junping Du, and S Yu Philip. 2019. Deep collaborative filtering with multi-aspect information in heterogeneous networks. IEEE Transactions on Knowledge and Data Engineering 33, 4 (2019), 1413\u20131425.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_77_2","first-page":"319","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Shi Shaoyun","year":"2020","unstructured":"Shaoyun Shi, Weizhi Ma, Min Zhang, Yongfeng Zhang, Xinxing Yu, Houzhi Shan, Yiqun Liu, and Shaoping Ma. 2020. Beyond user embedding matrix: Learning to hash for modeling large-scale users in recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 319\u2013328."},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_79_2","article-title":"Federated multi-task learning","author":"Smith Virginia","year":"2017","unstructured":"Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar. 2017. Federated multi-task learning. arXiv preprint arXiv:1705.10467 (2017).","journal-title":"arXiv preprint arXiv:1705.10467"},{"key":"e_1_3_2_80_2","article-title":"Ldp-fl: Practical private aggregation in federated learning with local differential privacy","author":"Sun Lichao","year":"2020","unstructured":"Lichao Sun, Jianwei Qian, Xun Chen, and Philip S Yu. 2020. Ldp-fl: Practical private aggregation in federated learning with local differential privacy. arXiv preprint arXiv:2007.15789 (2020).","journal-title":"arXiv preprint arXiv:2007.15789"},{"key":"e_1_3_2_81_2","first-page":"837","volume-title":"Proceedings of The Web Conference 2020","author":"Sun Peijie","year":"2020","unstructured":"Peijie Sun, Le Wu, Kun Zhang, Yanjie Fu, Richang Hong, and Meng Wang. 2020. Dual learning for explainable recommendation: Towards unifying user preference prediction and review generation. In Proceedings of The Web Conference 2020. 837\u2013847."},{"key":"e_1_3_2_82_2","article-title":"Towards federated graph learning for collaborative financial crimes detection","author":"Suzumura Toyotaro","year":"2019","unstructured":"Toyotaro Suzumura, Yi Zhou, Natahalie Baracaldo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Yuji Watanabe, Pablo Loyola, et\u00a0al. 2019. Towards federated graph learning for collaborative financial crimes detection. arXiv preprint arXiv:1909.12946 (2019).","journal-title":"arXiv preprint arXiv:1909.12946"},{"key":"e_1_3_2_83_2","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1109\/BigData47090.2019.9005465","volume-title":"2019 IEEE International Conference on Big Data (Big Data)","author":"Triastcyn Aleksei","year":"2019","unstructured":"Aleksei Triastcyn and Boi Faltings. 2019. Federated learning with bayesian differential privacy. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2587\u20132596."},{"key":"e_1_3_2_84_2","first-page":"1","volume-title":"Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security","author":"Truex Stacey","year":"2019","unstructured":"Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou. 2019. A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security. 1\u201311."},{"key":"e_1_3_2_85_2","first-page":"513","article-title":"The EU general data protection regulation: Toward a property regime for protecting data privacy","volume":"123","author":"Victor Jacob M","year":"2013","unstructured":"Jacob M Victor. 2013. The EU general data protection regulation: Toward a property regime for protecting data privacy. Yale LJ 123 (2013), 513.","journal-title":"Yale LJ"},{"key":"e_1_3_2_86_2","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1145\/1390156.1390294","volume-title":"Proceedings of the 25th International Conference on Machine Learning","author":"Vincent Pascal","year":"2008","unstructured":"Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning. 1096\u20131103."},{"issue":"12","key":"e_1_3_2_87_2","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.","volume":"11","author":"Vincent Pascal","year":"2010","unstructured":"Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and L\u00e9on Bottou. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research 11, 12 (2010).","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"e_1_3_2_88_2","first-page":"221","article-title":"European union data privacy law reform: General data protection regulation, privacy shield, and the right to delisting","volume":"72","author":"Voss W Gregory","year":"2016","unstructured":"W Gregory Voss. 2016. European union data privacy law reform: General data protection regulation, privacy shield, and the right to delisting. The Business Lawyer 72, 1 (2016), 221\u2013234.","journal-title":"The Business Lawyer"},{"key":"e_1_3_2_89_2","first-page":"6153","volume-title":"34nd AAAI Conference on Artificial Intelligence, AAAI 2020","author":"Wang Fei","year":"2020","unstructured":"Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, and Shijin Wang. 2020. Neural cognitive diagnosis for intelligent education systems. In 34nd AAAI Conference on Artificial Intelligence, AAAI 2020. 6153\u20136161."},{"key":"e_1_3_2_90_2","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1109\/INFOCOM41043.2020.9155494","volume-title":"IEEE INFOCOM 2020-IEEE Conference on Computer Communications","author":"Wang Hao","year":"2020","unstructured":"Hao Wang, Zakhary Kaplan, Di Niu, and Baochun Li. 2020. Optimizing federated learning on non-iid data with reinforcement learning. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 1698\u20131707."},{"issue":"4","key":"e_1_3_2_91_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3086677","article-title":"Understanding the purpose of permission use in mobile apps","volume":"35","author":"Wang Haoyu","year":"2017","unstructured":"Haoyu Wang, Yuanchun Li, Yao Guo, Yuvraj Agarwal, and Jason I Hong. 2017. Understanding the purpose of permission use in mobile apps. ACM Transactions on Information Systems (TOIS) 35, 4 (2017), 1\u201340.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_2_92_2","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1145\/3292500.3330931","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Wang Hao","year":"2019","unstructured":"Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, and Wen Su. 2019. MCNE: An end-to-end framework for learning multiple conditional network representations of social network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1064\u20131072."},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6096"},{"key":"e_1_3_2_94_2","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","article-title":"Federated learning with differential privacy: Algorithms and performance analysis","volume":"15","author":"Wei Kang","year":"2020","unstructured":"Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H Yang, Farhad Farokhi, Shi Jin, Tony QS Quek, and H Vincent Poor. 2020. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security 15 (2020), 3454\u20133469.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_3_2_95_2","first-page":"6572","volume-title":"IJCAI","author":"Wei Xiguang","year":"2019","unstructured":"Xiguang Wei, Quan Li, Yang Liu, Han Yu, Tianjian Chen, and Qiang Yang. 2019. Multi-agent visualization for explaining federated learning.. In IJCAI. 6572\u20136574."},{"key":"e_1_3_2_96_2","article-title":"Fedgnn: Federated graph neural network for privacy-preserving recommendation","author":"Wu Chuhan","year":"2021","unstructured":"Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, and Xing Xie. 2021. Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925 (2021).","journal-title":"arXiv preprint arXiv:2102.04925"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441747"},{"key":"e_1_3_2_98_2","first-page":"957","volume-title":"Proceedings of the Web Conference 2021","author":"Wu Jinze","year":"2021","unstructured":"Jinze Wu, Qi Liu, Zhenya Huang, Yuting Ning, Hao Wang, Enhong Chen, Jinfeng Yi, and Bowen Zhou. 2021. Hierarchical personalized federated learning for user modeling. In Proceedings of the Web Conference 2021. 957\u2013968."},{"key":"e_1_3_2_99_2","first-page":"115","volume-title":"Companion Proceedings of the The Web Conference 2018","author":"Wu Peizhi","year":"2018","unstructured":"Peizhi Wu, Yi Tu, Zhenglu Yang, Adam Jatowt, and Masato Odagaki. 2018. Deep modeling of the evolution of user preferences and item attributes in dynamic social networks. In Companion Proceedings of the The Web Conference 2018. 115\u2013116."},{"key":"e_1_3_2_100_2","article-title":"Federated graph classification over non-iid graphs","volume":"34","author":"Xie Han","year":"2021","unstructured":"Han Xie, Jing Ma, Li Xiong, and Carl Yang. 2021. Federated graph classification over non-iid graphs. Advances in Neural Information Processing Systems 34 (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"2","key":"e_1_3_2_101_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1462198.1462203","article-title":"User language model for collaborative personalized search","volume":"27","author":"Xue Gui-Rong","year":"2009","unstructured":"Gui-Rong Xue, Jie Han, Yong Yu, and Qiang Yang. 2009. User language model for collaborative personalized search. ACM Transactions on Information Systems (TOIS) 27, 2 (2009), 1\u201328.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_16"},{"issue":"2","key":"e_1_3_2_103_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298981","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang Qiang","year":"2019","unstructured":"Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 2 (2019), 1\u201319.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_104_2","doi-asserted-by":"publisher","DOI":"10.2200\/S00960ED2V01Y201910AIM043"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.5555\/3275299"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1145\/2699670"},{"key":"e_1_3_2_107_2","article-title":"Federated unsupervised representation learning","author":"Zhang Fengda","year":"2020","unstructured":"Fengda Zhang, Kun Kuang, Zhaoyang You, Tao Shen, Jun Xiao, Yin Zhang, Chao Wu, Yueting Zhuang, and Xiaolin Li. 2020. Federated unsupervised representation learning. arXiv preprint arXiv:2010.08982 (2020).","journal-title":"arXiv preprint arXiv:2010.08982"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2882995"},{"issue":"1","key":"e_1_3_2_109_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TSMC.2017.2665038","article-title":"A sequential approach to market state modeling and analysis in online p2p lending","volume":"48","author":"Zhao Hongke","year":"2017","unstructured":"Hongke Zhao, Qi Liu, Hengshu Zhu, Yong Ge, Enhong Chen, Yan Zhu, and Junping Du. 2017. A sequential approach to market state modeling and analysis in online p2p lending. 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