{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T18:16:45Z","timestamp":1785953805155,"version":"3.56.0"},"reference-count":62,"publisher":"Informa UK Limited","issue":"1","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["112-2121-M-002-008"],"award-info":[{"award-number":["112-2121-M-002-008"]}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"crossref","award":["JCYJ20200109141235597"],"award-info":[{"award-number":["JCYJ20200109141235597"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61761136008"],"award-info":[{"award-number":["61761136008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Annals of GIS"],"published-print":{"date-parts":[[2026,1,2]]},"DOI":"10.1080\/19475683.2026.2617187","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T13:06:31Z","timestamp":1768914391000},"page":"19-35","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":1,"title":["Federated LSTM-based deep learning model for privacy-preserving predictions of human trajectories across multiple data providers"],"prefix":"10.1080","volume":"32","author":[{"given":"Alessandro","family":"Crivellari","sequence":"first","affiliation":[{"name":"National Taiwan University","place":["Taipei, Taiwan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhui","family":"Shi","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"301","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/17489725.2016.1169322"},{"key":"e_1_3_4_3_1","volume-title":"Proceedings of the European conference on computer vision (ECCV) workshops","author":"Bisagno N.","year":"2018","unstructured":"Bisagno, N., B. Zhang, and N. Conci. 2018. \u201cGroup LSTM: Group Trajectory Prediction in Crowded Scenarios.\u201d In Proceedings of the European conference on computer vision (ECCV) workshops, Munich, Germany."},{"key":"e_1_3_4_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.06.007"},{"key":"e_1_3_4_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467158"},{"key":"e_1_3_4_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2015.55"},{"key":"e_1_3_4_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394138"},{"key":"e_1_3_4_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06605-9_16"},{"key":"e_1_3_4_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339564"},{"key":"e_1_3_4_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-1629-6_4"},{"key":"e_1_3_4_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/su12010349"},{"key":"e_1_3_4_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/09540091.2025.2458502"},{"key":"e_1_3_4_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37210-0_6"},{"key":"e_1_3_4_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2013.03.006"},{"key":"e_1_3_4_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-8983-6_32"},{"key":"e_1_3_4_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00983"},{"key":"e_1_3_4_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcan.2023.01.022"},{"key":"e_1_3_4_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1868470.1868479"},{"key":"e_1_3_4_19_1","doi-asserted-by":"publisher","DOI":"10.5311\/JOSIS.2019.19.510"},{"key":"e_1_3_4_20_1","doi-asserted-by":"publisher","DOI":"10.1080\/17489725.2021.1900612"},{"key":"e_1_3_4_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3127712"},{"key":"e_1_3_4_22_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0170907"},{"key":"e_1_3_4_23_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_4_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"e_1_3_4_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-7509-4_3"},{"key":"e_1_3_4_26_1","first-page":"5132","volume-title":"International conference on machine learning, PMLR","author":"Karimireddy S. P.","year":"2020","unstructured":"Karimireddy, S. P. 2020. \u201cScaffold: Stochastic Controlled Averaging for Federated Learning.\u201d In International conference on machine learning, PMLR, 5132\u20135143."},{"key":"e_1_3_4_27_1","first-page":"28663","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Karimireddy S. P.","year":"2021","unstructured":"Karimireddy, S. P. 2021. \u201cBreaking the Centralized Barrier for Cross-Device Federated Learning.\u201d In Advances in Neural Information Processing Systems, vol. 34, 28663\u201328676. Virtual conference."},{"key":"e_1_3_4_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.03.034"},{"key":"e_1_3_4_29_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1412.6980"},{"key":"e_1_3_4_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2017.03.002"},{"key":"e_1_3_4_31_1","first-page":"429","volume-title":"Proceedings of Machine Learning and Systems","volume":"2","author":"Li T.","year":"2020","unstructured":"Li, T. 2020. \u201cFederated Optimization in Heterogeneous Networks.\u201d In Proceedings of Machine Learning and Systems, vol. 2, 429\u2013450. Austin, TX, USA."},{"key":"e_1_3_4_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2976321"},{"key":"e_1_3_4_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.08.001"},{"key":"e_1_3_4_34_1","doi-asserted-by":"publisher","DOI":"10.3390\/su11061669"},{"key":"e_1_3_4_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.07.010"},{"key":"e_1_3_4_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-33783-8_10"},{"key":"e_1_3_4_37_1","first-page":"1273","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","author":"McMahan B.","year":"2017","unstructured":"McMahan, B. 2017. \u201cCommunication-Efficient Learning of Deep Networks from Decentralized Data.\u201d In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273\u20131282. Fort Lauderdale, FL, USA."},{"key":"e_1_3_4_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2558446"},{"key":"e_1_3_4_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2014.6848002"},{"key":"e_1_3_4_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2011.5767898"},{"key":"e_1_3_4_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00982"},{"key":"e_1_3_4_42_1","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12769"},{"key":"e_1_3_4_43_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2023.2262550"},{"key":"e_1_3_4_44_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0253868"},{"key":"e_1_3_4_45_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00489-2"},{"key":"e_1_3_4_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"e_1_3_4_47_1","doi-asserted-by":"publisher","DOI":"10.1111\/gean.12144"},{"key":"e_1_3_4_48_1","doi-asserted-by":"publisher","DOI":"10.2991\/icaita-16.2016.24"},{"key":"e_1_3_4_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2018.04.073"},{"key":"e_1_3_4_50_1","first-page":"21111","volume-title":"International Conference on Machine Learning","author":"Tang Z.","year":"2022","unstructured":"Tang, Z. 2022. \u201cVirtual Homogeneity Learning: Defending Against Data Heterogeneity in Federated Learning.\u201d In International Conference on Machine Learning, PMLR: 21111\u201321132."},{"key":"e_1_3_4_51_1","unstructured":"TensorFlow. 2020. \u201cTensorFlow Federated: Machine Learning on Decentralized Data.\u201d https:\/\/www.tensorflow.org\/federated."},{"key":"e_1_3_4_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3075706"},{"key":"e_1_3_4_53_1","first-page":"6281","article-title":"Minibatch Vs Local SGD for Heterogeneous Distributed Learning","volume":"33","author":"Woodworth B. E.","year":"2020","unstructured":"Woodworth, B. E., K. K. Patel, and N. Srebro. 2020. \u201cMinibatch Vs Local SGD for Heterogeneous Distributed Learning.\u201d Advances in Neural Information Processing Systems 33: 6281\u20136292.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_4_54_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29763-x"},{"key":"e_1_3_4_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.12.016"},{"key":"e_1_3_4_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tbs.2013.12.002"},{"key":"e_1_3_4_57_1","volume-title":"Advances in Neural Information Processing Systems","author":"Zaheer M.","year":"2018","unstructured":"Zaheer, M. 2018. \u201cAdaptive Methods for Nonconvex Optimization.\u201d In Advances in Neural Information Processing Systems, Vol. 31. Montreal, Canada."},{"issue":"3","key":"e_1_3_4_58_1","first-page":"4474","article-title":"TD-MDB: A Truth Discovery Based Multi-Dimensional Bidding Strategy for Federated Learning in Industrial IoT Systems","volume":"11","author":"Zeng P.","year":"2023","unstructured":"Zeng, P. 2023. \u201cTD-MDB: A Truth Discovery Based Multi-Dimensional Bidding Strategy for Federated Learning in Industrial IoT Systems.\u201d IEEE Internet of Things Journal 11 (3): 4474\u20134288.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_4_59_1","first-page":"15383","article-title":"Why are Adaptive Methods Good for Attention Models?","volume":"33","author":"Zhang J.","year":"2020","unstructured":"Zhang, J. 2020. \u201cWhy are Adaptive Methods Good for Attention Models?\u201d Advances in Neural Information Processing Systems 33: 15383\u201315393.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_4_60_1","first-page":"26293","volume-title":"International Conference on Machine Learning","author":"Zhang X.","year":"2022","unstructured":"Zhang, X. 2022. \u201cPersonalized Federated Learning via Variational Bayesian Inference.\u201d In International Conference on Machine Learning, 26293\u201326310, New Orleans, LA, USA: PMLR."},{"key":"e_1_3_4_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2017.12.004"},{"key":"e_1_3_4_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2021.103406"},{"key":"e_1_3_4_63_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30952-7_35"}],"container-title":["Annals of GIS"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/19475683.2026.2617187","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:25:58Z","timestamp":1772774758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/19475683.2026.2617187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,2]]},"references-count":62,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,2]]}},"alternative-id":["10.1080\/19475683.2026.2617187"],"URL":"https:\/\/doi.org\/10.1080\/19475683.2026.2617187","relation":{},"ISSN":["1947-5683","1947-5691"],"issn-type":[{"value":"1947-5683","type":"print"},{"value":"1947-5691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,2]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tagi20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tagi20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2025-03-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-31","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}