{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T01:10:18Z","timestamp":1730337018579,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","funder":[{"name":"Key R&D Program of Shandong Province","award":["2022CXGC020107"]},{"name":"the National Natural Science Foundation of China (NSFC)","award":["62102232","62122042"]},{"name":"Shandong Science Fund for Excellent Young Scholars","award":["2023HWYQ-007"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,14]]},"DOI":"10.1145\/3641512.3686375","type":"proceedings-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T21:11:22Z","timestamp":1727817082000},"page":"151-160","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Federating from History in Streaming Federated Learning"],"prefix":"10.1145","author":[{"ORCID":"http:\/\/orcid.org\/0009-0006-2346-2031","authenticated-orcid":false,"given":"Ruirui","family":"Zhang","sequence":"first","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4579-5380","authenticated-orcid":false,"given":"Yifei","family":"Zou","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8907-2064","authenticated-orcid":false,"given":"Zhenzhen","family":"Xie","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0824-9284","authenticated-orcid":false,"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4981-0496","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"the University of Aizu, Aizuwakamatsu, Japan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6017-975X","authenticated-orcid":false,"given":"Zhipeng","family":"Cai","sequence":"additional","affiliation":[{"name":"Georiga State University, Atlanta, United States of America"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5912-4647","authenticated-orcid":false,"given":"Xiuzhen","family":"Cheng","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6835-5981","authenticated-orcid":false,"given":"Dongxiao","family":"Yu","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Adaptive Federated Learning in Presence of Concept Drift. In IJCNN","author":"Canonaco G.","year":"2021","unstructured":"G. Canonaco, A. Bergamasco, A. Mongelluzzo, and M. Roveri. 2021. Adaptive Federated Learning in Presence of Concept Drift. In IJCNN 2021. IEEE, 1--7."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-11219-x"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Y. Chen Z. Chai Y. Cheng and H. Rangwala. 2021. Asynchronous Federated Learning for Sensor Data with Concept Drift. In IEEE BigData 2021. IEEE 4822--4831.","DOI":"10.1109\/BigData52589.2021.9671924"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"e_1_3_2_1_5_1","unstructured":"C. T. Dinh N. H. Tran and T. D. Nguyen. 2020. Personalized Federated Learning with Moreau Envelopes. CoRR abs\/2006.08848 (2020)."},{"key":"e_1_3_2_1_6_1","volume-title":"Online Data Selection for Federated Learning with Limited Storage. In WWW","author":"Gong C.","year":"2023","unstructured":"C. Gong, Z. Zheng, F. Wu, Y. Shao, B. Li, and G. Chen. 2023. To Store or Not? Online Data Selection for Federated Learning with Limited Storage. In WWW 2023. ACM, 3044--3055."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2023.103475"},{"key":"e_1_3_2_1_8_1","volume-title":"BMSB","author":"Hassan S.","year":"2023","unstructured":"S. Hassan, A. Brennan, G. Muntean, and J. McManis. 2023. User Profile-Based Viewport Prediction Using Federated Learning in Real-Time 360-Degree Video Streaming. In BMSB 2023. 1--7."},{"key":"e_1_3_2_1_9_1","unstructured":"T. Hsu H. Qi and M. Brown. 2019. Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification. CoRR abs\/1909.06335 (2019)."},{"key":"e_1_3_2_1_10_1","volume-title":"Dynamic Scheduling For Federated Edge Learning With Streaming Data. In ICASSPW","author":"Hu C.","year":"2023","unstructured":"C. Hu, Z. Chen, and E. Larsson. 2023. Dynamic Scheduling For Federated Edge Learning With Streaming Data. In ICASSPW 2023. 1--5."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118421"},{"key":"e_1_3_2_1_12_1","volume-title":"AISTATS 2023 (Proceedings of Machine Learning Research","volume":"5853","author":"Jothimurugesan E.","unstructured":"E. Jothimurugesan, K. Hsieh, J. Wang, G. Joshi, and P. B. Gibbons. 2023. Federated Learning under Distributed Concept Drift. In AISTATS 2023 (Proceedings of Machine Learning Research, Vol. 206). PMLR, 5834--5853."},{"key":"e_1_3_2_1_13_1","unstructured":"A. Krizhevsky G. Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_14_1","unstructured":"T. Li A. Kumar Sahu M. Zaheer M. Sanjabi A. Talwalkar and V. Smith. 2020. Federated Optimization in Heterogeneous Networks. In MLSys 2020."},{"key":"e_1_3_2_1_15_1","volume-title":"ICLR","author":"Li X.","year":"2021","unstructured":"X. Li, M.Jiang, X. Zhang, M. Kamp, and Q. Dou. 2021. FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. In ICLR 2021."},{"key":"e_1_3_2_1_16_1","volume-title":"Federated Learning for Data Streams. In AISTATS 2023 (Proceedings of Machine Learning Research","volume":"8924","author":"Marfoq O.","unstructured":"O. Marfoq, G. Neglia, L. Kameni, and R. Vidal. 2023. Federated Learning for Data Streams. In AISTATS 2023 (Proceedings of Machine Learning Research, Vol. 206). PMLR, 8889--8924."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2023.3293462"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119235"},{"key":"e_1_3_2_1_19_1","volume-title":"AISTATS 2017 (Proceedings of Machine Learning Research","volume":"1282","author":"McMahan B.","year":"2017","unstructured":"B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Ag\u00fcera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In AISTATS 2017 (Proceedings of Machine Learning Research, Vol. 54). PMLR, 1273--1282."},{"key":"e_1_3_2_1_20_1","unstructured":"NIWA. 2024. New Zealand's National Climate Database. https:\/\/cliflo.niwa.co.nz\/ Dataset."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1080\/09540099550039318"},{"key":"e_1_3_2_1_22_1","volume-title":"CIKM","author":"Tao Y.","year":"2009","unstructured":"Y. Tao and M. T. Ozsu. 2009. Mining data streams with periodically changing distributions. In CIKM 2009. 887--896."},{"key":"e_1_3_2_1_23_1","first-page":"58","article-title":"The problem of concept drift: definitions and related work","volume":"106","author":"Tsymbal A.","year":"2004","unstructured":"A. Tsymbal. 2004. The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin 106, 2 (2004), 58.","journal-title":"Computer Science Department, Trinity College Dublin"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3327316"},{"key":"e_1_3_2_1_25_1","volume-title":"Accurate Forgetting for Heterogeneous Federated Continual Learning. In ICLR","author":"Wuerkaixi A.","year":"2024","unstructured":"A. Wuerkaixi, S. Cui, J. Zhang, K. Yan, B. Han, G. Niu, L. Fang, C. Zhang, and M. Sugiyama. 2024. Accurate Forgetting for Heterogeneous Federated Continual Learning. In ICLR 2024."},{"key":"e_1_3_2_1_26_1","unstructured":"H. Xiao K. Rasul and R. Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms."},{"key":"e_1_3_2_1_27_1","volume-title":"Using Federated Learning in Anomaly Detection and Analytics on Real-time Streaming Data of Healthcare. In ICGSP 2023 (ICGSP '23)","author":"Yogitha M","year":"2023","unstructured":"M Yogitha and KS Srinivas. 2023. Using Federated Learning in Anomaly Detection and Analytics on Real-time Streaming Data of Healthcare. In ICGSP 2023 (ICGSP '23). Association for Computing Machinery, 29--34."},{"key":"e_1_3_2_1_28_1","volume-title":"Federated Continual Learning with Adaptive Parameter Communication. CoRR abs\/2003.03196","author":"Yoon J.","year":"2020","unstructured":"J. Yoon, W. Jeong, G. Lee, E. Yang, and S. Ju Hwang. 2020. Federated Continual Learning with Adaptive Parameter Communication. CoRR abs\/2003.03196 (2020)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"R.R. Zhang et al. 2024. Technical Report of Federating from History in Streaming Federated Learning. https:\/\/github.com\/SheryMo\/Technical-Report-of-FedHIST","DOI":"10.1145\/3641512.3686375"},{"key":"e_1_3_2_1_30_1","unstructured":"Met \u00c9ireann. 2024. Dublin Climate Database. https:\/\/www.met.ie\/climate\/available-data\/historical-data Dataset."}],"event":{"name":"MobiHoc '24: Twenty-fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"],"location":"Athens Greece","acronym":"MobiHoc '24"},"container-title":["Proceedings of the Twenty-fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3641512.3686375","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:44:49Z","timestamp":1730335489000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3641512.3686375"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":30,"alternative-id":["10.1145\/3641512.3686375","10.1145\/3641512"],"URL":"https:\/\/doi.org\/10.1145\/3641512.3686375","relation":{},"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"2024-10-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}