{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T02:30:05Z","timestamp":1772505005160,"version":"3.50.1"},"reference-count":48,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"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":[[2023,10,1]]},"DOI":"10.1109\/iros55552.2023.10341467","type":"proceedings-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T19:17:55Z","timestamp":1702495075000},"page":"10141-10148","source":"Crossref","is-referenced-by-count":3,"title":["CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction"],"prefix":"10.1109","author":[{"given":"Umar","family":"Khalid","sequence":"first","affiliation":[{"name":"University of Central Florida,Center For Research in Computer Vision,Orlando,FL,USA"}]},{"given":"Hasan","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Wayne State University,Dept. of Computer Science,Detroit,MI,USA"}]},{"given":"Saeed","family":"Vahidian","sequence":"additional","affiliation":[{"name":"Duke University,Dept. of Electrical and Computer Engineering,Durham,NC,USA"}]},{"given":"Jing","family":"Hua","sequence":"additional","affiliation":[{"name":"Wayne State University,Dept. of Computer Science,Detroit,MI,USA"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Central Florida,Center For Research in Computer Vision,Orlando,FL,USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/2376.001.0001"},{"issue":"4","key":"ref2","first-page":"847","article-title":"Industrial robots: A survey on the recent progress and future directions","volume":"6","author":"He","year":"2019","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9981793"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2022.104193"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/HRI.2019.8673256"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00433"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00821"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCSW53096.2021.00012"},{"issue":"1","key":"ref9","first-page":"702","article-title":"Pre-trained federated learning: A comprehensive survey","volume":"24","author":"Li","year":"2022","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/APSIPA.2014.7041588"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1080\/01691864.2019.1636714"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956300"},{"issue":"5","key":"ref13","first-page":"1175","article-title":"Convolutional neural networks for hand gesture recognition in industrial human-robot interaction","volume":"32","author":"Huang","year":"2021","journal-title":"Journal of Intelligent Manufacturing"},{"key":"ref14","first-page":"14","article-title":"Learning grasping affordances from visual cues for industrial robotics","volume":"112","author":"Rizzi","year":"2019","journal-title":"Robotics and Autonomous Systems"},{"key":"ref15","first-page":"101927","article-title":"Deep learning for visual perception of robotic manipulators in industrial environments","volume":"64","author":"Spina","year":"2020","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"key":"ref16","article-title":"Structured pruning for efficient neural network inference","volume-title":"International Conference on Learning Representations","author":"Liu","year":"2019"},{"key":"ref17","first-page":"2790","article-title":"Parameter-efficient transfer learning for nlp","volume-title":"International Conference on Machine Learning","author":"Houlsby"},{"key":"ref18","article-title":"Aim: Adapting image models for efficient video action recognition","author":"Yang","year":"2023","journal-title":"arXiv preprint"},{"key":"ref19","article-title":"Parameter-Efficient Image-to-Video Transfer Learning","author":"Pan","year":"2022","journal-title":"arXiv"},{"key":"ref20","article-title":"Adaptformer: Adapting vision transformers for scalable visual recognition","author":"Chen","year":"2022","journal-title":"arXivpreprint"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref22","article-title":"Towards building the federated GPT: federated instruction tuning","volume":"abs\/2305.05644","author":"Zhang","year":"2023","journal-title":"CoRR"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"ref24","first-page":"11285","article-title":"TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Cai"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.1"},{"key":"ref26","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics","author":"McMahan"},{"key":"ref27","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018","journal-title":"arXiv preprint"},{"key":"ref28","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine learning and systems","volume":"2","author":"Li","year":"2020"},{"key":"ref29","first-page":"377","article-title":"Fedadapt: Overcoming model degradation for federated learning with non-iid data","volume-title":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","author":"Li"},{"key":"ref30","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018","journal-title":"arXiv preprint"},{"key":"ref31","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Wang"},{"key":"ref32","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","volume-title":"International Conference on Machine Learning","author":"Karimireddy"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118510"},{"key":"ref34","first-page":"184","article-title":"Deploying a human robot interaction model for dementia care in federated learning","volume-title":"2022 IEEE\/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","author":"Su","year":"2022"},{"key":"ref35","author":"Dosovitskiy","year":"2021","journal-title":"An Image is Worth 16\u00d716 Words: Transformers for Image Recognition at Scale"},{"key":"ref36","article-title":"AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition","author":"Chen","year":"2022","journal-title":"arXiv"},{"key":"ref37","article-title":"Offsite-tuning: Transfer learning without full model","author":"Xiao","year":"2023","journal-title":"arXiv preprint"},{"key":"ref38","first-page":"23296","article-title":"Intriguing properties of vision transformers","volume":"34","author":"Naseer","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref39","article-title":"The kinetics human action video dataset","author":"Kay","year":"2017","journal-title":"arXiv preprint"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICHMS49158.2020.9209531"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00130"},{"key":"ref42","article-title":"Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training","author":"Tong","year":"2022","journal-title":"arXiv preprint"},{"key":"ref43","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume-title":"International conference on machine learning","author":"Ioffe"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.1"},{"key":"ref45","article-title":"When do curricula work in federated learning?","volume":"abs\/2212.12712","author":"Vahidian","year":"2022","journal-title":"CoRR"},{"key":"ref46","article-title":"On Pre-Training for Federated Learning","author":"Chen","year":"2022","journal-title":"arXiv"},{"key":"ref47","article-title":"Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated Learning","volume-title":"arXiv","author":"Nguyen","year":"2022"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00028"}],"event":{"name":"2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","location":"Detroit, MI, USA","start":{"date-parts":[[2023,10,1]]},"end":{"date-parts":[[2023,10,5]]}},"container-title":["2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10341341\/10341342\/10341467.pdf?arnumber=10341467","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T22:54:26Z","timestamp":1703026466000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10341467\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,1]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/iros55552.2023.10341467","relation":{},"subject":[],"published":{"date-parts":[[2023,10,1]]}}}