{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:10:06Z","timestamp":1755861006187,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"PEPR MOBIDEC Mob Sci-Dat Factory","award":["ANR-23- PEMO-0004"],"award-info":[{"award-number":["ANR-23- PEMO-0004"]}]},{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico (CNPq)"},{"name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais (FAPEMIG)"},{"name":"CAPES-STIC-AMSUD LINT project","award":["22-STIC-07"],"award-info":[{"award-number":["22-STIC-07"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,29]]},"DOI":"10.1145\/3678717.3691323","type":"proceedings-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T06:29:21Z","timestamp":1732256961000},"page":"325-337","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Beauty or Beast: Human Behavioral Insights and Learning Power of Federated Mobility Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1885-8772","authenticated-orcid":false,"given":"Jo\u00e3o Paulo","family":"Esper","sequence":"first","affiliation":[{"name":"Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1483-6269","authenticated-orcid":false,"given":"Aline Carneiro","family":"Viana","sequence":"additional","affiliation":[{"name":"Inria, Palaiseau, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9142-2919","authenticated-orcid":false,"given":"Jussara M.","family":"Almeida","sequence":"additional","affiliation":[{"name":"Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"}]}],"member":"320","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/3283445"},{"key":"e_1_3_2_1_2_1","article-title":"Revealing an inherently limiting factor in human mobility prediction","author":"Amichi L.","year":"2022","unstructured":"Amichi, L., Carneiro, A. V., Crovella, M., and Loureiro, A. Revealing an inherently limiting factor in human mobility prediction. IEEE Transactions on Emerging Topics in Computing (2022).","journal-title":"IEEE Transactions on Emerging Topics in Computing ("},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397536.3422248"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474717.3484220"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30553-3"},{"key":"e_1_3_2_1_6_1","volume-title":"Survey of federated learning models for spatial-temporal mobility applications. arXiv preprint arXiv:2305.05257","author":"Belal Y.","year":"2023","unstructured":"Belal, Y., Mokhtar, S. B., Haddadi, H., Wang, J., and Mashhadi, A. Survey of federated learning models for spatial-temporal mobility applications. arXiv preprint arXiv:2305.05257 (2023)."},{"key":"e_1_3_2_1_7_1","volume-title":"Flower: A Friendly Federated Learning Research Framework. arXiv preprint arXiv:2007.14390","author":"Beutel D. J.","year":"2020","unstructured":"Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Fernandez-Marques, J., Gao, Y., Sani, L., Li, K. H., Parcollet, T., de Gusm\u00e3o, P. P. B., et al. Flower: A Friendly Federated Learning Research Framework. arXiv preprint arXiv:2007.14390 (2020)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.4337\/9781784713591"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2805845"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-019-0206-8"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102481"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-017-0129-1"},{"key":"e_1_3_2_1_13_1","first-page":"1","article-title":"On estimating the predictability of human mobility: the role of routine","volume":"10","author":"do Couto Teixeira D.","year":"2021","unstructured":"do Couto Teixeira, D., Almeida, J. M., and Viana, A. C. On estimating the predictability of human mobility: the role of routine. EPJ Data Science 10, 1 (2021), 49.","journal-title":"EPJ Data Science"},{"key":"e_1_3_2_1_14_1","first-page":"5","author":"Ezequiel C. E. J.","year":"2022","unstructured":"Ezequiel, C. E. J., Gjoreski, M., and Langheinrich, M. Federated Learning for Privacy-Aware Human Mobility Modeling. Frontiers in Artificial Intelligence 5 (2022).","journal-title":"Federated Learning for Privacy-Aware Human Mobility Modeling. Frontiers in Artificial Intelligence"},{"key":"e_1_3_2_1_15_1","first-page":"4","article-title":"Decentralized attention-based personalized human mobility prediction. Proceedings of the ACM on Interactive, Mobile","volume":"3","author":"Fan Z.","year":"2020","unstructured":"Fan, Z., Song, X., Jiang, R., Chen, Q., and Shibasaki, R. Decentralized attention-based personalized human mobility prediction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 4 (2020), 1--26.","journal-title":"Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186058"},{"key":"e_1_3_2_1_17_1","first-page":"1","article-title":"PMF: A privacy-preserving human mobility prediction framework via federated learning. Proceedings of the ACM on Interactive, Mobile","volume":"4","author":"Feng J.","year":"2020","unstructured":"Feng, J., Rong, C., Sun, F., Guo, D., and Li, Y. PMF: A privacy-preserving human mobility prediction framework via federated learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1--21.","journal-title":"Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2181196.2181199"},{"key":"e_1_3_2_1_19_1","volume-title":"-L. Understanding individual human mobility patterns. nature 453, 7196","author":"Gonzalez M. C.","year":"2008","unstructured":"Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. Understanding individual human mobility patterns. nature 453, 7196 (2008), 779--782."},{"key":"e_1_3_2_1_20_1","volume-title":"Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604","author":"Hard A.","year":"2018","unstructured":"Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-024-00460-7"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505821.2505828"},{"key":"e_1_3_2_1_23_1","volume-title":"Privacy-preserving patient similarity learning in a federated environment: development and analysis. JMIR medical informatics 6, 2","author":"Lee J.","year":"2018","unstructured":"Lee, J., Sun, J., Wang, F., Wang, S., Jun, C.-H., Jiang, X., et al. Privacy-preserving patient similarity learning in a federated environment: development and analysis. JMIR medical informatics 6, 2 (2018), e7744."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397536.3422270"},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of Machine learning and systems 2","author":"Li T.","year":"2020","unstructured":"Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems 2 (2020), 429--450."},{"key":"e_1_3_2_1_26_1","first-page":"965","volume-title":"Federated Learning on Non-IID Data Silos: An Experimental Study. In 2022 IEEE 38th international conference on data engineering (ICDE)","author":"Li T.","year":"2022","unstructured":"Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V. Federated Learning on Non-IID Data Silos: An Experimental Study. In 2022 IEEE 38th international conference on data engineering (ICDE) (2022), IEEE, pp. 965--978."},{"key":"e_1_3_2_1_27_1","volume-title":"A survey on deep learning for human mobility. ACM Computing Surveys (CSUR) 55, 1","author":"Luca M.","year":"2021","unstructured":"Luca, M., Barlacchi, G., Lepri, B., and Pappalardo, L. A survey on deep learning for human mobility. ACM Computing Surveys (CSUR) 55, 1 (2021), 1--44."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370421"},{"key":"e_1_3_2_1_29_1","first-page":"1273","volume-title":"PMLR","author":"McMahan B.","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (2017), PMLR, pp. 1273--1282."},{"key":"e_1_3_2_1_30_1","volume-title":"On the Regularity of Human Mobility. Pervasive and Mobile Computing","author":"Mucceli E.","year":"2016","unstructured":"Mucceli, E., Carneiro Viana, A., Sarraute, C., Brea, J., and Alvarez-Hamelin, J. I. On the Regularity of Human Mobility. Pervasive and Mobile Computing (2016)."},{"key":"e_1_3_2_1_31_1","volume-title":"Planet dump","author":"OpenStreetMap","year":"2017","unstructured":"OpenStreetMap contributors. Planet dump retrieved from https:\/\/planet.osm.org. https:\/\/www.openstreetmap.org, 2017."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v103.i04"},{"key":"e_1_3_2_1_33_1","first-page":"1","volume-title":"FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","author":"Park C.","year":"2023","unstructured":"Park, C., Choi, T., Kim, T., Cho, M., Hong, J., Choi, M., and Choo, J. FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (2023), pp. 1--10."},{"key":"e_1_3_2_1_34_1","first-page":"780","volume-title":"Federated Learning for Human Mobility. In 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)","author":"Proteasa V.-A.","year":"2023","unstructured":"Proteasa, V.-A., Ciobanu, R.-I., Dobre, C., and Marin, R.-C. Federated Learning for Human Mobility. In 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) (2023), IEEE, pp. 780--785."},{"key":"e_1_3_2_1_35_1","volume-title":"Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20","author":"Rousseeuw P. J.","year":"1987","unstructured":"Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20 (1987), 53--65."},{"key":"e_1_3_2_1_36_1","volume-title":"Clustered federated learning: Modelagnostic distributed multitask optimization under privacy constraints","author":"Sattler F.","year":"2020","unstructured":"Sattler, F., M\u00fcller, K.-R., and Samek, W. Clustered federated learning: Modelagnostic distributed multitask optimization under privacy constraints. IEEE transactions on neural networks and learning systems 32, 8 (2020), 3710--3722."},{"key":"e_1_3_2_1_37_1","volume-title":"Urban Computing Leveraging Location-Based Social Network Data: a Survey. ACM Computing Surveys (Mar","author":"Silva T. H.","year":"2019","unstructured":"Silva, T. H., Carneiro Viana, A., Benevenuto, F., Villas, L., Salles, J., Loureiro, A. A. F., and Quercia, D. Urban Computing Leveraging Location-Based Social Network Data: a Survey. ACM Computing Surveys (Mar. 2019)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/PerCom.2014.6813948"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1177170"},{"key":"e_1_3_2_1_40_1","volume-title":"What are data silos and what problems do they cause? https:\/\/www.techtarget.com\/searchdatamanagement\/definition\/data-silo","author":"Stedman C.","year":"2021","unstructured":"Stedman, C. What are data silos and what problems do they cause? https:\/\/www.techtarget.com\/searchdatamanagement\/definition\/data-silo, 2021. [Accessed on 07-May-2024]."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459625"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3347146.3359093"},{"key":"e_1_3_2_1_43_1","volume-title":"Location prediction with personalized federated learning. Soft Computing","author":"Wang S.","year":"2022","unstructured":"Wang, S., Wang, B., Yao, S., Qu, J., and Pan, Y. Location prediction with personalized federated learning. Soft Computing (2022), 1--12."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/302"},{"key":"e_1_3_2_1_45_1","volume-title":"Enhanced handover mechanism using mobility prediction in wireless networks. PloS one 15, 1","author":"Yap K.-L.","year":"2020","unstructured":"Yap, K.-L., Chong, Y.-W., and Liu, W. Enhanced handover mechanism using mobility prediction in wireless networks. PloS one 15, 1 (2020), e0227982."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2735960.2735985"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/IOTM.004.2100182"},{"key":"e_1_3_2_1_48_1","article-title":"Federated representation learning with data heterogeneity for human mobility prediction","author":"Zhang X.","year":"2023","unstructured":"Zhang, X., Wang, Q., Ye, Z., Ying, H., and Yu, D. Federated representation learning with data heterogeneity for human mobility prediction. IEEE Transactions on Intelligent Transportation Systems (2023).","journal-title":"IEEE Transactions on Intelligent Transportation Systems ("},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"}],"event":{"name":"SIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"],"location":"Atlanta GA USA","acronym":"SIGSPATIAL '24"},"container-title":["Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678717.3691323","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3678717.3691323","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T10:40:45Z","timestamp":1755859245000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3678717.3691323"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,29]]},"references-count":49,"alternative-id":["10.1145\/3678717.3691323","10.1145\/3678717"],"URL":"https:\/\/doi.org\/10.1145\/3678717.3691323","relation":{},"subject":[],"published":{"date-parts":[[2024,10,29]]},"assertion":[{"value":"2024-11-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}