{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T01:33:14Z","timestamp":1718587994005},"reference-count":23,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003382","name":"Japan Science and Technology Agency (JST) Core Research for Evolutional Science and Technology (CREST), Japan","doi-asserted-by":"publisher","award":["JPMJCR20F2"],"id":[{"id":"10.13039\/501100003382","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3093382","type":"journal-article","created":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T19:48:55Z","timestamp":1624996135000},"page":"92986-92998","source":"Crossref","is-referenced-by-count":9,"title":["An On-Device Federated Learning Approach for Cooperative Model Update Between Edge Devices"],"prefix":"10.1109","volume":"9","author":[{"given":"Rei","family":"Ito","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2426-4516","authenticated-orcid":false,"given":"Mineto","family":"Tsukada","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9578-3842","authenticated-orcid":false,"given":"Hiroki","family":"Matsutani","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2787987"},{"key":"ref12","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","author":"lin","year":"2017","journal-title":"arXiv 1712 01887"},{"key":"ref13","article-title":"Federated learning with non-IID data","author":"zhao","year":"2018","journal-title":"arXiv 1806 00582"},{"key":"ref14","first-page":"1","article-title":"Overcoming forgetting in federated learning on non-IID data","author":"shoham","year":"2019","journal-title":"Proc Int Workshop Federated Learn User Privacy"},{"key":"ref15","first-page":"1","article-title":"Abnormal client behavior detection in federated learning","author":"li","year":"2019","journal-title":"Proc Int Workshop Federated Learn User Privacy Data Confidentiality"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2991079.2991125"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CSE\/EUC.2019.00087"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref19","first-page":"684","article-title":"A selective model aggregation approach in federated learning for online anomaly detection","author":"qin","year":"2020","journal-title":"Proc Int Conferences Internet Things (iThings)"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"hinton","year":"2006","journal-title":"Science"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2006.880583"},{"key":"ref6","first-page":"985","article-title":"Extreme learning machine: A new learning scheme of feedforward neural networks","author":"huang","year":"2004","journal-title":"Proc IEEE Int Joint Conf Neural Netw"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.09.075"},{"key":"ref8","article-title":"Federated learning: Strategies for improving communication efficiency","author":"konecn\u00fd","year":"2016","journal-title":"arXiv 1610 05492"},{"key":"ref7","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"konecn\u00fd","year":"2016","journal-title":"arXiv 1610 02527"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.2973631"},{"key":"ref1","first-page":"518","article-title":"OS-ELM-FPGA: An FPGA-based online sequential unsupervised anomaly detector","author":"tsukada","year":"2018","journal-title":"Proc Int Eur Conf Parallel Distrib Comput"},{"key":"ref9","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2016","journal-title":"arXiv 1602 05629"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2016.7795584"},{"key":"ref22","author":"lecun","year":"2010","journal-title":"MNIST Handwritten Digit Database"},{"key":"ref21","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","author":"anguita","year":"2013","journal-title":"Proc Eur Symp Artif Neural Netw Comput Intell Mach Learn (ESANN)"},{"key":"ref23","author":"abadi","year":"2021","journal-title":"TensorFlow Large-Scale Machine Learning on Heterogeneous Systems"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09467340.pdf?arnumber=9467340","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:49Z","timestamp":1639771009000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9467340\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3093382","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}