Abstract
Point-of-Interest (POI) recommendation is significant in location-based social networks to help users discover new locations of interest. Previous studies on such recommendation mainly adopted a centralized learning framework where check-in data were uploaded, trained and predicted centrally in the cloud. However, such a framework suffers from privacy risks caused by check-in data exposure and fails to meet real-time recommendation needs when the data volume is huge and communication is blocked in crowded places. In this paper, we propose PREFER, an edge-accelerated federated learning framework for POI recommendation. It decouples the recommendation into two parts. Firstly, to protect privacy, users train local recommendation models and share multi-dimensional user-independent parameters instead of check-in data. Secondly, to improve recommendation efficiency, we aggregate these distributed parameters on edge servers in proximity to users (such as base stations) instead of remote cloud servers. We implement the PREFER prototype and evaluate its performance using two real-world datasets and two POI recommendation models. Extensive experiments demonstrate that PREFER strengthens privacy protection and improves efficiency with little sacrifice to recommendation quality compared to centralized learning. It achieves the best quality and efficiency and is more compatible with increasingly sophisticated POI recommendation models compared to other state-of-the-art privacy-preserving baselines.
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- [n.d.]. AidLearning. Retrieved May 27, 2020 from http://www.aidlearning.net/index.htmlGoogle Scholar
- 2019. Robot 4.0 white paper: robot system and architecture of cloud-edge-end integration. Technical Report. Intel. http://robotplaces.mikecrm.com/kdQ63AdGoogle Scholar
- Miguel E. Andrés, Nicolás Emilio Bordenabe, Konstantinos Chatzikokolakis, and Catuscia Palamidessi. 2013. Geo-indistinguishability: differential privacy for location-based systems. In 2013 ACM SIGSAC Conference on Computer and Communications Security, CCS'13, Berlin, Germany, November 4-8, 2013. 901--914. https://doi.org/10.1145/2508859.2516735Google ScholarDigital Library
- Louise Barkhuus. 2012. The mismeasurement of privacy: using contextual integrity to reconsider privacy in HCI. In CHI Conference on Human Factors in Computing Systems, CHI '12, Austin, TX, USA - May 05-10, 2012. 367--376. https://doi.org/10.1145/2207676.2207727Google ScholarDigital Library
- Betim Berjani and Thorsten Strufe. 2011. A recommendation system for spots in location-based online social networks. In Proceedings of the 4th Workshop on Social Network Systems, Salzburg, Austria, April 10, 2011. 4. https://doi.org/10.1145/1989656.1989660Google ScholarDigital Library
- Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander. 2019. Towards Federated Learning at Scale: System Design. CoRR abs/1902.01046 (2019). arXiv:1902.01046 http://arxiv.org/abs/1902.01046Google Scholar
- Ioannis Boutsis and Vana Kalogeraki. 2016. Location privacy for crowdsourcing applications. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, Heidelberg, Germany, September 12-16, 2016. 694--705. https://doi.org/10.1145/2971648.2971741Google ScholarDigital Library
- Hancheng Cao, Jie Feng, Yong Li, and Vassilis Kostakos. 2018. Uniqueness in the City: Urban Morphology and Location Privacy. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 2 (2018), 62:1-62:20. https://doi.org/10.1145/3214265Google ScholarDigital Library
- Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, and Xiaolong Li. 2018. Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, the 30th innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, Louisiana, USA, February 2-7, 2018. 257--264. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16123Google Scholar
- Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2012. Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, July 22-26, 2012, Toronto, Ontario, Canada. http://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/4748Google Scholar
- Qiang Cui, Yuyuan Tang, Shu Wu, and Liang Wang. 2019. Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction. In Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part III. 289--301. https://doi.org/10.1007/978-3-030-16142-2_23Google Scholar
- Koustabh Dolui, Illapha Cuba Gyllensten, Dietwig Lowet, Sam Michiels, Hans Hallez, and Danny Hughes. 2019. Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization. In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2019, Seoul, Republic of Korea, June 17-21, 2019. 624--625. https://doi.org/10.1145/3307334.3328657Google ScholarDigital Library
- Jie Feng, Can Rong, Funing Sun, Diansheng Guo, and Yong Li. 2020. PMF: A Privacy-preserving Human Mobility Prediction Framework via Federated Learning. IMWUT 4, 1 (2020), 10:1-10:21. https://doi.org/10.1145/3381006Google ScholarDigital Library
- Shanshan Feng, Gao Cong, Bo An, and Yeow Meng Chee. 2017. POI2Vec: Geographical Latent Representation for Predicting Future Visitors. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA. 102--108. http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14902Google ScholarCross Ref
- Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized Ranking Metric Embedding for Next New POI Recommendation. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, Qiang Yang and Michael J. Wooldridge (Eds.). AAAI Press, 2069--2075. http://ijcai.org/Abstract/15/293Google Scholar
- Chen Gao, Chao Huang, Yue Yu, Huandong Wang, Yong Li, and Depeng Jin. 2019. Privacy-preserving Cross-domain Location Recommendation. IMWUT 3, 1 (2019), 11:1-11:21. https://doi.org/10.1145/3314398Google Scholar
- Jean-Benoît Griesner, Talel Abdessalem, and Hubert Naacke. 2015. POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences. In Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015, Vienna, Austria, September 16-20, 2015. 301--304. https://dl.acm.org/citation.cfm?id=2799679Google ScholarDigital Library
- Yeting Guo, Fang Liu, Zhiping Cai, Li Chen, and Nong Xiao. 2020. FEEL: A Federated Edge Learning System for Efficient and Privacy-Preserving Mobile Healthcare. In ICPP 2020: 49th International Conference on Parallel Processing, Edmonton, AB, Canada, August 17-20, 2020. 9:1-9:11. https://doi.org/10.1145/3404397.3404410Google Scholar
- István Hegedüs, Gábor Danner, and Márk Jelasity. 2019. Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning. In Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I. 317--332. https://doi.org/10.1007/978-3-030-43823-4_27Google Scholar
- Teruo Higashino, Hirozumi Yamaguchi, Akihito Hiromori, Akira Uchiyama, and Keiichi Yasumoto. 2017. Edge Computing and IoT Based Research for Building Safe Smart Cities Resistant to Disasters. In 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017, Atlanta, GA, USA, June 5-8, 2017. 1729--1737. https://doi.org/10.1109/ICDCS.2017.160Google ScholarCross Ref
- Saeid Hosseini and Lei Thor Li. 2016. Point-Of-Interest Recommendation Using Temporal Orientations of Users and Locations. In Database Systems for Advanced Applications - 21st International Conference, DASFAA 2016, Dallas, TX, USA, April 16-19, 2016, Proceedings, Part I. 330--347. https://doi.org/10.1007/978-3-319-32025-0_21Google Scholar
- Yi-Hsuan Hung, Chih-Yu Wang, and Ren-Hung Hwang. 2020. Optimizing Social Welfare of Live Video Streaming Services in Mobile Edge Computing. IEEE Trans. Mob. Comput. 19, 4 (2020), 922--934. https://doi.org/10.1109/TMC.2019.2901786Google ScholarDigital Library
- Congfeng Jiang, Yeliang Qiu, Honghao Gao, Tiantian Fan, Kangkang Li, and Jian Wan. 2019. An Edge Computing Platform for Intelligent Operational Monitoring in Internet Data Centers. IEEE Access 7 (2019), 133375--133387. https://doi.org/10.1109/ACCESS.2019.2939614Google ScholarCross Ref
- Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian. 2007. t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. In Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, The Marmara Hotel, Istanbul, Turkey, April 15-20, 2007. 106--115. https://doi.org/10.1109/ICDE.2007.367856Google ScholarCross Ref
- Fang Liu, Guoming Tang, Youhuizi Li, Zhiping Cai, Xingzhou Zhang, and Tongqing Zhou. 2019. A Survey on Edge Computing Systems and Tools. Proc. IEEE 107, 8 (2019), 1537--1562. https://doi.org/10.1109/JPROC.2019.2920341Google ScholarCross Ref
- Lumin Liu, Jun Zhang, S. H. Song, and Khaled Ben Letaief. 2019. Edge-Assisted Hierarchical Federated Learning with Non-IID Data. CoRR abs/1905.06641 (2019). arXiv:1905.06641 http://arxiv.org/abs/1905.06641Google Scholar
- Yanchi Liu, Chuanren Liu, Bin Liu, Meng Qu, and Hui Xiong. 2016. Unified Point-of-Interest Recommendation with Temporal Interval Assessment. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016. 1015--1024. https://doi.org/10.1145/2939672.2939773Google ScholarDigital Library
- King's College London. 2019. King's and NVIDIA join forces to build UK's rst Al platform for hospitals. Technical Report. King's College London. King's College London. https://www.kcl.ac.uk/news/kings-and-nvidia-to-build-uks-first-al-platformGoogle Scholar
- Sidi Lu, Yongtao Yao, and Weisong Shi. 2019. Collaborative Learning on the Edges: A Case Study on Connected Vehicles. In 2nd USENIX Workshop on Hot Topics in Edge Computing, Renton, WA, USA, July 9, 2019. https://www.usenix.org/conference/hotedge19/presentation/luGoogle Scholar
- Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer, and Muthuramakrishnan Venkitasubramaniam. 2006. l-Diversity: Privacy Beyond k-Anonymity. In Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, 3-8 April 2006, Atlanta, GA, USA. 24. https://doi.org/10.1109/ICDE.2006.1Google ScholarDigital Library
- Takayuki Nishio and Ryo Yonetani. 2019. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge. In 2019 IEEE International Conference on Communications, ICC 2019, Shanghai, China, May 20-24, 2019. 1--7. https://doi.org/10.1109/ICC.2019.8761315Google Scholar
- Guanhua Qiao, Supeng Leng, Sabita Maharjan, Yan Zhang, and Nirwan Ansari. 2020. Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks. IEEE Internet of Things Journal 7, 1 (2020), 247--257. https://doi.org/10.1109/JIOT.2019.2945640Google ScholarCross Ref
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009. 452--461. https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=1630&proceeding_id=25Google ScholarDigital Library
- Sumudu Samarakoon, Mehdi Bennis, Walid Saad, and Mérouane Debbah. 2020. Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications. IEEE Trans. Communications 68, 2 (2020), 1146--1159. https://doi.org/10.1109/TCOMM.2019.2956472Google ScholarCross Ref
- Constantine Stephanidis (Ed.). 2019. HCI International 2019 - Posters - 21st International Conference, HCII 2019, Orlando, FL, USA, July 26-31, 2019, Proceedings, Part III. Communications in Computer and Information Science, Vol. 1034. Springer. https://doi.org/10.1007/978-3-030-23525-3Google Scholar
- Chang Su, Yumeng Chen, and Xianzhong Xie. 2019. Location Recommendation with Privacy Protection. ISMSI 2019: Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics Swarm Intelligence, 83--91. https://doi.org/10.1145/3325773.3325787Google Scholar
- Sukhmani Sukhmani, Mohammad Sadeghi, Melike Erol-Kantarci, and Abdulmotaleb El-Saddik. 2019. Edge Caching and Computing in 5G for Mobile AR/VR and Tactile Internet. IEEE Multim. 26, 1 (2019), 21--30. https://doi.org/10.1109/MMUL.2018.2879591Google ScholarCross Ref
- Latanya Sweeney. 2002. k-Anonymity: A Model for Protecting Privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, 5 (2002), 557--570. https://doi.org/10.1142/S0218488502001648Google ScholarDigital Library
- Zeyi Tao and Qun Li. 2018. eSGD: Communication Efficient Distributed Deep Learning on the Edge. In USENIX Workshop on Hot Topics in Edge Computing, HotEdge 2018, Boston, MA, July 10, 2018. https://www.usenix.org/conference/hotedge18/presentation/taoGoogle Scholar
- China Telecom. [n.d.]. China Telecom edge computing technology white paper. ([n. d.]).Google Scholar
- Zhen Tu, Kai Zhao, Fengli Xu, Yong Li, Li Su, and Depeng Jin. 2019. Protecting Trajectory From Semantic Attack Considering ${k}$ -Anonymity, ${l}$ -Diversity, and ${t}$ -Closeness. IEEE Trans. Netw. Serv. Manag. 16, 1 (2019), 264--278. https://doi.org/10.1109/TNSM.2018.2877790Google ScholarDigital Library
- Qinyong Wang, Hongzhi Yin, Tong Chen, Zi Huang, Hao Wang, Yanchang Zhao, and Nguyen Quoc Viet Hung. 2020. Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020. 906--916. https://doi.org/10.1145/3366423.3380170Google Scholar
- Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, and Kevin Chan. 2018. When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning. In 2018 IEEE Conference on Computer Communications, INFOCOM 2018, Honolulu, HI, USA, April 16-19, 2018. 63--71. https://doi.org/10.1109/INFOCOM.2018.8486403Google Scholar
- Xiaofei Wang, Yiwen Han, Chenyang Wang, Qiyang Zhao, Xu Chen, and Min Chen. 2019. In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning. IEEE Netw. 33, 5 (2019), 156--165. https://doi.org/10.1109/MNET.2019.1800286Google ScholarDigital Library
- Xiwei Wang, Hao Yang, and Kiho Lim. 2018. Privacy-Preserving POI Recommendation Using Nonnegative Matrix Factorization. In 2018 IEEE Symposium on Privacy-Aware Computing, PAC 2018, Washington, DC, USA, September 26-28, 2018. 117--118. https://doi.org/10.1109/PAC.2018.00018Google Scholar
- Webank. 2019. An Industrial Level Federated Learning Framework. Retrieved February 28, 2020 from https://github.com/FederatedAI/FATEGoogle Scholar
- Xiguang Wei, Quan Li, Yang Liu, Han Yu, Tianjian Chen, and Qiang Yang. 2019. Multi-Agent Visualization for Explaining Federated Learning. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019. 6572--6574. https://doi.org/10.24963/ijcai.2019/960Google ScholarCross Ref
- Di Xiao, Min Li, and Hongying Zheng. 2020. Smart Privacy Protection for Big Video Data Storage Based on Hierarchical Edge Computing. Sensors 20, 5 (2020), 1517. https://doi.org/10.3390/s20051517Google ScholarCross Ref
- Yinhao Xiao, Yizhen Jia, Chun-Chi Liu, Xiuzhen Cheng, Jiguo Yu, and Weifeng Lv. 2019. Edge Computing Security: State of the Art and Challenges. Proc. IEEE 107, 8 (2019), 1608--1631. https://doi.org/10.1109/JPROC.2019.2918437Google Scholar
- Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu, and Depeng Jin. 2017. Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data. In Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3-7, 2017. 1241--1250. https://doi.org/10.1145/3038912.3052620Google ScholarDigital Library
- Dongdong Ye, Rong Yu, Miao Pan, and Zhu Han. 2020. Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach. IEEE Access 8 (2020), 23920--23935. https://doi.org/10.1109/ACCESS.2020.2968399Google ScholarCross Ref
- Jun Zeng, Feng Li, Xin He, and Junhao Wen. 2019. Fused Collaborative Filtering With User Preference, Geographical and Social Influence for Point of Interest Recommendation. Int. J. Web Service Res. 16, 4 (2019), 40--52. https://doi.org/10.4018/IJWSR.2019100103Google ScholarCross Ref
- Shenglin Zhao, Irwin King, and Michael R. Lyu. 2016. A Survey of Point-of-interest Recommendation in Location-based Social Networks. CoRR abs/1607.00647 (2016). arXiv:1607.00647 http://arxiv.org/abs/1607.00647Google Scholar
Index Terms
- PREFER: Point-of-interest REcommendation with efficiency and privacy-preservation via Federated Edge leaRning
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