{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:46:40Z","timestamp":1773514000384,"version":"3.50.1"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,27]]},"DOI":"10.23919\/cnsm67658.2025.11297529","type":"proceedings-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T18:40:10Z","timestamp":1766428810000},"page":"1-7","source":"Crossref","is-referenced-by-count":1,"title":["MVFL: Multivariate Vertical Federated Learning for Time-Series Forecasting"],"prefix":"10.23919","author":[{"given":"Xicun","family":"Yang","sequence":"first","affiliation":[{"name":"SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JunePyo","family":"Jung","sequence":"additional","affiliation":[{"name":"Institut Polytechnique de Paris,LTCI, Telecom Paris,Palaiseau,France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialiang","family":"Lu","sequence":"additional","affiliation":[{"name":"SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keun-Woo","family":"Lim","sequence":"additional","affiliation":[{"name":"Institut Polytechnique de Paris,LTCI, Telecom Paris,Palaiseau,France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leonardo","family":"Linguaglossa","sequence":"additional","affiliation":[{"name":"Institut Polytechnique de Paris,LTCI, Telecom Paris,Palaiseau,France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3147602"},{"key":"ref2","first-page":"479","article-title":"Medfuse: Multi-modal fusion with clinical time-series data and chest x-ray images","volume-title":"Machine Learning for Healthcare Conference.","author":"Hayat"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/s21134511"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1080\/24725854.2018.1555383"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/app10124102"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"ref8","article-title":"Federated learning: Opportunities and challenges","author":"Mammen","year":"2021","journal-title":"arXiv preprint arXiv:2101.05428"},{"key":"ref9","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017","journal-title":"Artificial intelligence and statistics."},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.23919\/CNSM59352.2023.10327817"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.23919\/CNSM59352.2023.10327839"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref13","article-title":"Towards federated learning at scale: Syste m design","author":"Bonawitz","year":"2019","journal-title":"arXiv preprint arXiv:1902.01046"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.23919\/CNSM55787.2022.9964646"},{"key":"ref15","article-title":"Pyvertical: A vertical federated learning framework for multi-headed splitnn","author":"Romanini","year":"2021","journal-title":"arXiv preprint arXiv:2104.00489"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3072238"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3352628"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2022.102474"},{"key":"ref19","article-title":"Vafl: a method of vertical asynchronous federated learning","author":"Chen","year":"2020","journal-title":"arXiv preprint arXiv:2007.06081"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403298"},{"key":"ref21","article-title":"A vertical federated learning framework for horizontally partitioned labels","author":"Xia","year":"2021","journal-title":"arXiv preprint arXiv:2106.10056"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref23","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for longterm series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref24","article-title":"Revitalizing multivariate time series forecasting: Learnable decomposition with inter-series dependencies and intra-series variations modeling","author":"Yu","year":"2024","journal-title":"arXiv preprint arXiv:2402.12694"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/393"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.23919\/CNSM52442.2021.9615554"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3389\/frcmn.2021.657653"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM42002.2020.9322206"},{"key":"ref29","article-title":"Vflair: A research library and benchmark for vertical federated learning","author":"Zou","year":"2023","journal-title":"arXiv preprint arXiv:2310.09827"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"}],"event":{"name":"2025 21st International Conference on Network and Service Management (CNSM)","location":"Bologna, Italy","start":{"date-parts":[[2025,10,27]]},"end":{"date-parts":[[2025,10,31]]}},"container-title":["2025 21st International Conference on Network and Service Management (CNSM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11297200\/11297407\/11297529.pdf?arnumber=11297529","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T06:20:49Z","timestamp":1766470849000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11297529\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":30,"URL":"https:\/\/doi.org\/10.23919\/cnsm67658.2025.11297529","relation":{},"subject":[],"published":{"date-parts":[[2025,10,27]]}}}