{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:56:50Z","timestamp":1778893010237,"version":"3.51.4"},"reference-count":41,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"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":[[2021,11,15]]},"DOI":"10.1109\/idsta53674.2021.9660819","type":"proceedings-article","created":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T15:50:32Z","timestamp":1640965832000},"page":"105-113","source":"Crossref","is-referenced-by-count":1,"title":["Using Transfer Learning in Building Federated Learning Models on Edge Devices"],"prefix":"10.1109","author":[{"given":"Jordan","family":"Suzuki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saba F.","family":"Lameh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasaman","family":"Amannejad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","year":"0","journal-title":"Google colab"},{"key":"ref38","year":"0","journal-title":"Tff simulation datasets cifar100"},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v25i1.8090","article-title":"Heterogeneous transfer learning for image classification","author":"zhu","year":"2011","journal-title":"Twenty-Fifth AAAI Conference on Artificial Intelligence"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995702"},{"key":"ref31","article-title":"Learning with augmented features for heterogeneous domain adaptation","author":"duan","year":"2012"},{"key":"ref30","first-page":"1095","article-title":"Heterogeneous domain adaptation for multiple classes","author":"zhou","year":"2014","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1109\/CVPR.2009.5206848","article-title":"Imagenet: A large-scale hierarchical image database","author":"deng","year":"2009","journal-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58558-7_29"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2017.2720603"},{"key":"ref34","article-title":"Learning from multiple outlooks","author":"harel","year":"2010"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8647927"},{"key":"ref11","article-title":"A federated learning approach for mobile packet classification","author":"bakopoulou","year":"2019"},{"key":"ref40","year":"0","journal-title":"Keras"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013587"},{"key":"ref13","first-page":"756","article-title":"D&#x00EF;ot: A federated self-learning anomaly detection system for iot","author":"nguyen","year":"2019","journal-title":"2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1700332"},{"key":"ref15","year":"0","journal-title":"FeatureCloud Our vision"},{"key":"ref16","year":"0","journal-title":"Musketeer About"},{"key":"ref17","year":"2019","journal-title":"NVIDIA Clara"},{"key":"ref18","author":"authors","year":"2019","journal-title":"PaddleFL"},{"key":"ref19","author":"authors","year":"2019","journal-title":"Federated AI technology enabler"},{"key":"ref28","first-page":"1118","article-title":"Cross-language text classification using structural correspondence learning","author":"prettenhofer","year":"2010","journal-title":"Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref3","year":"0","journal-title":"CIFAR-100 dataset"},{"key":"ref27","article-title":"Heterogeneous domain adaptation using manifold alignment","author":"wang","year":"2011","journal-title":"Twenty-Second International Joint Conference on Artificial Intelligence"},{"key":"ref6","article-title":"Collaborative machine learning without centralized training data","year":"2017"},{"key":"ref29","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v28i1.8961","article-title":"Hybrid heterogeneous transfer learning through deep learning","author":"zhou","year":"2014","journal-title":"Twenty-Eighth AAAI Conference on Artificial Intelligence"},{"key":"ref5","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":"ref8","article-title":"Federated learning for mobile keyboard prediction","author":"hard","year":"2018"},{"key":"ref7","year":"0","journal-title":"private federated learning (neurips 2019 expo talk abstract)"},{"key":"ref2","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?ny?","year":"2016"},{"key":"ref1","year":"0","journal-title":"General Data Protection Regulation"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2991401"},{"key":"ref20","article-title":"A generic framework for privacy preserving deep learning","author":"ryffel","year":"2018"},{"key":"ref22","article-title":"Leaf: A benchmark for federated settings","author":"caldas","year":"2018"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412771"},{"key":"ref24","year":"0","journal-title":"TensorFlow Federated Learning"},{"key":"ref41","year":"0","journal-title":"Federated Learning for CIFAR-100"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.23919\/CNSM50824.2020.9269105"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412599"},{"key":"ref25","first-page":"954","article-title":"Cmfl: Mitigating communication overhead for federated learning","author":"luping","year":"2019","journal-title":"2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)"}],"event":{"name":"2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","location":"Tartu, Estonia","start":{"date-parts":[[2021,11,15]]},"end":{"date-parts":[[2021,11,17]]}},"container-title":["2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9660786\/9660789\/09660819.pdf?arnumber=9660819","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T03:11:41Z","timestamp":1674270701000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9660819\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,15]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/idsta53674.2021.9660819","relation":{},"subject":[],"published":{"date-parts":[[2021,11,15]]}}}