{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T18:09:31Z","timestamp":1783620571849,"version":"3.55.0"},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>We demonstrate FedTSC, a novel federated learning (FL) system for interpretable time series classification (TSC). FedTSC is an FL-based TSC solution that makes a great balance among security, interpretability, accuracy, and efficiency. We achieve this by first extending the concept of FL to consider both stronger security and model interpretability. Then, we propose three novel TSC methods based on explainable features to deal with the challengeable FL problem. To build the model in the FL setting, we propose several security protocols that are well optimized by maximally reducing the bottlenecked communication complexity. We build the FedTSC system based on such a solution, and provide the user Sklearn-like Python APIs for practical utility. We show that the system is easy to use, and the novel TSC approach is superior.<\/jats:p>","DOI":"10.14778\/3554821.3554875","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3686-3689","source":"Crossref","is-referenced-by-count":12,"title":["FedTSC"],"prefix":"10.14778","volume":"15","author":[{"given":"Zhiyu","family":"Liang","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-016-0483-9"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457241"},{"key":"e_1_2_1_3_1","volume-title":"On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and knowledge discovery 7, 4","author":"Keogh Eamonn","year":"2003","unstructured":"Eamonn Keogh and Shruti Kasetty . 2003. On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and knowledge discovery 7, 4 ( 2003 ), 349--371. Eamonn Keogh and Shruti Kasetty. 2003. On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and knowledge discovery 7, 4 (2003), 349--371."},{"key":"e_1_2_1_4_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273--1282.  Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273--1282."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-06057-9"},{"key":"e_1_2_1_6_1","unstructured":"Christoph Molnar. 2022. Interpretable Machine Learning (2 ed.). christophm.github.io\/interpretable-ml-book\/  Christoph Molnar. 2022. Interpretable Machine Learning (2 ed.). christophm.github.io\/interpretable-ml-book\/"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the VLDB Endowment 13","author":"Wu Yuncheng","unstructured":"Yuncheng Wu , Shaofeng Cai , Xiaokui Xiao , Gang Chen , and Beng Chin Ooi . [n.d.]. Privacy Preserving Vertical Federated Learning for Tree-based Models . Proceedings of the VLDB Endowment 13 , 11 ([n. d.]). Yuncheng Wu, Shaofeng Cai, Xiaokui Xiao, Gang Chen, and Beng Chin Ooi. [n.d.]. Privacy Preserving Vertical Federated Learning for Tree-based Models. Proceedings of the VLDB Endowment 13, 11 ([n. d.])."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3554821.3554875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:34:24Z","timestamp":1672227264000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3554821.3554875"}},"subtitle":["a secure federated learning system for interpretable time series classification"],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":8,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.14778\/3554821.3554875"],"URL":"https:\/\/doi.org\/10.14778\/3554821.3554875","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}