{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:37:24Z","timestamp":1772206644584,"version":"3.50.1"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&#x0026;D Program of China","award":["2022ZD0160703"],"award-info":[{"award-number":["2022ZD0160703"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202422"],"award-info":[{"award-number":["62202422"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372408"],"award-info":[{"award-number":["62372408"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021MH227"],"award-info":[{"award-number":["ZR2021MH227"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Artificial Intelligence Laboratory"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tmc.2024.3509915","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T19:13:48Z","timestamp":1733253228000},"page":"3302-3315","source":"Crossref","is-referenced-by-count":3,"title":["FedMTPP: Federated Multivariate Temporal Point Processes for Distributed Event Sequence Forecasting"],"prefix":"10.1109","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7470-8639","authenticated-orcid":false,"given":"Houxin","family":"Gong","sequence":"first","affiliation":[{"name":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1617-0920","authenticated-orcid":false,"given":"Haishuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7973-2746","authenticated-orcid":false,"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3645-1041","authenticated-orcid":false,"given":"Sheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7626-0162","authenticated-orcid":false,"given":"Hongyang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang Lab, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1097-2044","authenticated-orcid":false,"given":"Jiajun","family":"Bu","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"4547","article-title":"Coevolutionary latent feature processes for continuous-time user-item interactions","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449908"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2015.144"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467210"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2001-3"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00047"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/58.1.83"},{"key":"ref9","first-page":"6754","article-title":"The neural Hawkes process: A neurally self-modulating multivariate point process","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Mei"},{"key":"ref10","first-page":"11 692","article-title":"Transformer Hawkes process","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zuo"},{"key":"ref11","first-page":"11 183","article-title":"Self-attentive Hawkes process","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4149(79)90008-5"},{"key":"ref13","first-page":"1717","article-title":"Learning granger causality for hawkes processes","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref14","first-page":"1062","article-title":"Nonparametric regressive point processes based on conditional Gaussian processes","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref15","article-title":"Intensity-free learning of temporal point processes","author":"Shchur","year":"2019"},{"key":"ref16","first-page":"73","article-title":"Fast and flexible temporal point processes with triangular maps","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Shchur"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939875"},{"key":"ref18","article-title":"Language models can improve event prediction by few-shot abductive reasoning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Shi"},{"key":"ref19","article-title":"Prompt-augmented temporal point process for streaming event sequence","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Xue"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3543873.3587681"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21343"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/412"},{"key":"ref24","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref25","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. 3rd Conf. Mach. Learn. Syst.","author":"Li"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/app10082864"},{"key":"ref27","first-page":"18 839","article-title":"Federated graph classification over non-IID graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Xie"},{"key":"ref28","first-page":"6671","article-title":"Subgraph federated learning with missing neighbor generation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30714-9"},{"key":"ref30","article-title":"Split learning for health: Distributed deep learning without sharing raw patient data","author":"Vepakomma","year":"2018"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2018.8645549"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014139"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2021.3082561"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006000"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482361"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3523061"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3178443"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2022.3198176"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17301"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3180117"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.2196\/26598"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.3390\/a15080273"},{"key":"ref43","first-page":"2738","article-title":"Compressed-VFL: Communication-efficient learning with vertically partitioned data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Castiglia"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01001-9_13"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7755\/10916419\/10776027.pdf?arnumber=10776027","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T19:42:10Z","timestamp":1741376530000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10776027\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":44,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2024.3509915","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}