{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T05:39:58Z","timestamp":1776231598231,"version":"3.50.1"},"reference-count":299,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100008982","name":"National Priorities Research Program-Standard (NPRPS) Thirteen (13th) Cycle from the Qatar National Research Fund","doi-asserted-by":"publisher","award":["NPRP13S-0205-200265"],"award-info":[{"award-number":["NPRP13S-0205-200265"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Commun. Surv. Tutorials"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/comst.2022.3200740","type":"journal-article","created":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T19:33:48Z","timestamp":1661456028000},"page":"2366-2418","source":"Crossref","is-referenced-by-count":151,"title":["Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence"],"prefix":"10.1109","volume":"24","author":[{"given":"Emna","family":"Baccour","sequence":"first","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0211-2666","authenticated-orcid":false,"given":"Naram","family":"Mhaisen","sequence":"additional","affiliation":[{"name":"Machine Learning Department, Qatar University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3887-2520","authenticated-orcid":false,"given":"Alaa Awad","family":"Abdellatif","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7565-5253","authenticated-orcid":false,"given":"Aiman","family":"Erbad","sequence":"additional","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1583-7503","authenticated-orcid":false,"given":"Amr","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9766-0085","authenticated-orcid":false,"given":"Mounir","family":"Hamdi","sequence":"additional","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8972-8094","authenticated-orcid":false,"given":"Mohsen","family":"Guizani","sequence":"additional","affiliation":[{"name":"Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE"}]}],"member":"263","reference":[{"key":"ref275","doi-asserted-by":"publisher","DOI":"10.1145\/3373376.3378522"},{"key":"ref274","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220106"},{"key":"ref277","doi-asserted-by":"publisher","DOI":"10.5220\/0007922404390447"},{"key":"ref276","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3000374"},{"key":"ref271","doi-asserted-by":"publisher","DOI":"10.1145\/3154793"},{"key":"ref270","doi-asserted-by":"publisher","DOI":"10.1109\/TPSISA52974.2021.00002"},{"key":"ref273","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2967734"},{"key":"ref170","first-page":"2145","article-title":"Learning to communicate with deep multi-agent reinforcement learning","author":"foerster","year":"2016","journal-title":"Proc 30th Int Conf Neural Inf Process Syst"},{"key":"ref272","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP45357.2019.8969086"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2020.3027695"},{"key":"ref171","first-page":"1","article-title":"Learning to schedule communication in multi-agent reinforcement learning","author":"kim","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1724-z"},{"key":"ref173","year":"2021","journal-title":"openai\/multiagent-particle-envs"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2307881"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2589879"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOMWKSHPS50562.2020.9162964"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2652459"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/CDC40024.2019.9029257"},{"key":"ref169","article-title":"Communication-efficient distributed reinforcement learning","author":"chen","year":"2018","journal-title":"arXiv 1812 03239"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3027048"},{"key":"ref38","article-title":"Playing atari with deep reinforcement learning","author":"mnih","year":"2013","journal-title":"arXiv 1312 5602"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00137"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref31","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"arXiv 1409 1556"},{"key":"ref30","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref267","doi-asserted-by":"publisher","DOI":"10.1145\/3274694.3274696"},{"key":"ref37","author":"goodfellow","year":"2014","journal-title":"arXiv 1406 2661"},{"key":"ref268","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.31"},{"key":"ref36","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":"ref269","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2018.00027"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09825-6"},{"key":"ref288","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC51323.2021.9498967"},{"key":"ref287","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3165472"},{"key":"ref286","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3027314"},{"key":"ref285","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00013"},{"key":"ref284","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3118970"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2868748"},{"key":"ref283","doi-asserted-by":"publisher","DOI":"10.3390\/s20205796"},{"key":"ref180","first-page":"10","article-title":"Learning with symmetric label noise: The importance of being unhinged","author":"van rooyen","year":"2015","journal-title":"Proc NIPS"},{"key":"ref282","doi-asserted-by":"publisher","DOI":"10.1145\/3426745.3431337"},{"key":"ref281","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_7"},{"key":"ref280","article-title":"Adversarial neural network inversion via auxiliary knowledge alignment","author":"yang","year":"2019","journal-title":"arXiv 1902 08552"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2017.2680245"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2682082"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2875470"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2344674"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3066210"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2844308"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2699783"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2888698"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2946162"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2017.10.002"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2456899"},{"key":"ref29","volume":"1","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2016.7564868"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2019.8766715"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013289"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC48107.2020.9148355"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102801"},{"key":"ref278","first-page":"3320","article-title":"How transferable are features in deep neural networks?","author":"yosinski","year":"2014","journal-title":"Proc 27th Int Conf Neural Inf Process Syst Vol 2"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/AINA.2015.254"},{"key":"ref279","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.202"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2009.82"},{"key":"ref293","doi-asserted-by":"publisher","DOI":"10.1007\/s13346-021-00929-2"},{"key":"ref50","author":"abadi","year":"2015","journal-title":"TensorFlow Large-Scale Machine Learning on Heterogeneous Systems"},{"key":"ref292","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3070627"},{"key":"ref51","year":"2021","journal-title":"Caffe3"},{"key":"ref295","article-title":"Combining federated and active learning for communication-efficient distributed failure prediction in aeronautics","author":"aussel","year":"2020","journal-title":"arXiv 2001 07504"},{"key":"ref294","doi-asserted-by":"publisher","DOI":"10.1002\/adma.201901989"},{"key":"ref297","article-title":"Federated self-supervised learning for heterogeneous clients","author":"makhija","year":"2022","journal-title":"arXiv 2205 12493"},{"key":"ref296","doi-asserted-by":"publisher","DOI":"10.1017\/9781108571401"},{"key":"ref299","doi-asserted-by":"publisher","DOI":"10.1561\/2200000051"},{"key":"ref298","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09938-y"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2447"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2743240"},{"key":"ref156","first-page":"4295","article-title":"QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning","author":"rashid","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref155","first-page":"2085","article-title":"Value-decomposition networks for cooperative multi-agent learning based on team reward","author":"sunehag","year":"2018","journal-title":"Proc AAMAS"},{"key":"ref150","first-page":"1","article-title":"Stochastic multi-player bandit learning from player-dependent feedback","author":"wang","year":"2020","journal-title":"Proc ICML Workshop Real World Exp Des Act Learn"},{"key":"ref291","article-title":"ResiliNet: Failure-resilient inference in distributed neural networks","author":"yousefpour","year":"2020","journal-title":"arXiv 2002 07386"},{"key":"ref152","first-page":"709","article-title":"Dynamic programming for partially observable stochastic games","volume":"4","author":"hansen","year":"2004","journal-title":"Proc AAAI"},{"key":"ref290","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1007\/s10458-019-09421-1"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7952664"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1145\/3447380"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17156"},{"key":"ref149","first-page":"2917","article-title":"Federated multi-armed bandits with personalization","author":"shi","year":"2021","journal-title":"Proc 24th Int Conf Artif Intell Stat"},{"key":"ref289","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3079164"},{"key":"ref59","year":"2021","journal-title":"Prime air"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2016.7487175"},{"key":"ref57","article-title":"End to end learning for self-driving cars","author":"bojarski","year":"2016","journal-title":"arXiv 1604 07316 [cs]"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00207"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9010028"},{"key":"ref54","article-title":"MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems","author":"chen","year":"2015","journal-title":"arXiv 1512 01274"},{"key":"ref53","article-title":"DeepLearningKit&#x2014;An GPU optimized deep learning framework for apple&#x2019;s iOS, OS X and tvOS developed in metal and swift","author":"tveit","year":"2016","journal-title":"arXiv 1605 04614"},{"key":"ref52","year":"2021","journal-title":"Core ML"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"ref167","first-page":"5872","article-title":"Fully decentralized multi-agent reinforcement learning with networked agents","volume":"80","author":"zhang","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref166","first-page":"2681","article-title":"Deep decentralized multi-task multi-agent reinforcement learning under partial observability","author":"omidshafiei","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5957"},{"key":"ref164","article-title":"Learning when to communicate at scale in multiagent cooperative and competitive tasks","author":"singh","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref163","first-page":"7254","article-title":"Learning attentional communication for multi-agent cooperation","volume":"31","author":"jiang","year":"2018","journal-title":"Proc Int Conf Adv Neural Inf Process Syst"},{"key":"ref162","first-page":"9908","article-title":"Learning efficient multi-agent communication: An information bottleneck approach","volume":"119","author":"wang","year":"2020","journal-title":"Proc 37th Int Conf Mach Learn"},{"key":"ref161","article-title":"R-MADDPG for partially observable environments and limited communication","author":"wang","year":"2020","journal-title":"Proc RL4RealLife Workshop 36th Int Conf Mach Learn"},{"key":"ref160","first-page":"2961","article-title":"Actor-attention-critic for multi-agent reinforcement learning","author":"iqbal","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0148-7"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref6","year":"2017","journal-title":"Prepare to succeed with the Internet of Things"},{"key":"ref5","volume":"1","author":"bughin","year":"2018","journal-title":"Assessing the Economic Impact of Artificial Intelligence"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8647466"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11794"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2844341"},{"key":"ref49","year":"2019","journal-title":"Autonomio Talos [Computer Software]"},{"key":"ref157","first-page":"5887","article-title":"QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning","author":"son","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3296957.3173191"},{"key":"ref158","first-page":"6382","article-title":"Multi-agent actor-critic for mixed cooperative-competitive environments","author":"lowe","year":"2017","journal-title":"Proc 31st Int Conf Neural Inf Process Syst"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3359789.3359824"},{"key":"ref45","article-title":"Lets keep it simple, using simple architectures to outperform deeper and more complex architectures","author":"hasanpour","year":"2018","journal-title":"arXiv 1608 06037"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref47","first-page":"480","article-title":"Data poisoning attacks against federated learning systems","author":"tolpegin","year":"2020","journal-title":"Proc Eur Symp Res Comput Security"},{"key":"ref42","article-title":"Proximal policy optimization algorithms","author":"schulman","year":"2017","journal-title":"arXiv 1707 06347"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"mnih","year":"2015","journal-title":"Nature"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ISVLSI.2019.00105"},{"key":"ref43","first-page":"1928","article-title":"Asynchronous methods for deep reinforcement learning","author":"mnih","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2984887"},{"key":"ref72","article-title":"Machine learning at the network edge: A survey","author":"murshed","year":"2020","journal-title":"arXiv 1908 00080"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2918951"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2921977"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2018.2840971"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2976000"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CSCI46756.2018.00177"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2977374"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2988367"},{"key":"ref79","article-title":"Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial","author":"feriani","year":"2020","journal-title":"arXiv 2011 03615"},{"key":"ref60","year":"2020","journal-title":"Uber Self-Driving Cars"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2019.05.086"},{"key":"ref61","year":"2021","journal-title":"Amazon Alexa"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.17775\/CSEEJPES.2018.00520"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2902887"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900272"},{"key":"ref66","author":"lavalle","year":"2017","journal-title":"Virtual Reality"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.3390\/app10144735"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.3024783"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2970550"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-27562-4_6"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2018.2858384"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2017.7927211"},{"key":"ref193","article-title":"Distributed deep convolutional neural networks for the Internet-of-Things","author":"disabato","year":"2019","journal-title":"arXiv 1908 01656"},{"key":"ref194","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3032443"},{"key":"ref195","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2018.00049"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2972000"},{"key":"ref95","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc 20th Int Conf Artif Intell Stat (AISTATS)"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3086014"},{"key":"ref190","first-page":"1737","article-title":"Deep learning with limited numerical precision","author":"gupta","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-016-0355-x"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2856261"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-012-0035-5"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2745201"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/OJCS.2020.2992630"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.23919\/JCC.2020.09.009"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/ICOIN48656.2020.9016505"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2940820"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.3007787"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2941458"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3003307"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2019.2904897"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1145\/3377454"},{"key":"ref89","article-title":"Federated learning for Internet of Things: Recent advances, taxonomy, and open challenges","author":"khan","year":"2020","journal-title":"arXiv 2009 13012"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000530"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3075439"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3013541"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3030072"},{"key":"ref200","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8485905"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2971981"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013160"},{"key":"ref209","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2018.8639121"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1145\/3093337.3037698"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1145\/3229556.3229562"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.1145\/3097895.3097903"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906396"},{"key":"ref207","doi-asserted-by":"publisher","DOI":"10.1109\/ICPADS47876.2019.00069"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1109\/SOCC46988.2019.1570554761"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1800506"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_40"},{"key":"ref210","doi-asserted-by":"publisher","DOI":"10.1109\/PADSW.2018.8645013"},{"key":"ref212","first-page":"1","article-title":"Improving device-edge cooperative inference of deep learning via 2-step pruning","author":"shi","year":"2019","journal-title":"Proc IEEE INFOCOM Conf Comput Commun Workshops (INFOCOM WKSHPS)"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737614"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539792225297"},{"key":"ref215","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipl.2009.02.017"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2947893"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.226"},{"key":"ref218","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9014122"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.1900305"},{"key":"ref220","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2000015"},{"key":"ref222","doi-asserted-by":"publisher","DOI":"10.1145\/3363347.3363366"},{"key":"ref221","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2018.2880978"},{"key":"ref229","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9149094"},{"key":"ref228","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2014.08.009"},{"key":"ref227","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-56252-0_2"},{"key":"ref226","doi-asserted-by":"publisher","DOI":"10.3390\/fi11100209"},{"key":"ref225","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2893250"},{"key":"ref224","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2019.2894703"},{"key":"ref223","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155237"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3021006"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2020.3014385"},{"key":"ref125","author":"oscar","year":"0","journal-title":"CRAWDAD Dataset"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/JCN.2020.000015"},{"key":"ref129","author":"dua","year":"0","journal-title":"UCI Machine Learning Repository"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOMW.2018.8406985"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref133","author":"sutton","year":"2018","journal-title":"Reinforcement Learning An Introduction"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1017\/9781108571401"},{"key":"ref131","article-title":"CINIC-10 is not ImageNet or CIFAR-10","author":"darlow","year":"2018","journal-title":"arXiv 1810 03505"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2020.2997852"},{"key":"ref232","author":"sahoo","year":"2018","journal-title":"Grouped convolutions&#x2014;Convolutions in parallel"},{"key":"ref233","article-title":"Musical chair: Efficient real-time recognition using collaborative IoT devices","author":"hadidi","year":"2018","journal-title":"arXiv 1802 02138"},{"key":"ref230","doi-asserted-by":"publisher","DOI":"10.1109\/CAHPC.2018.8645927"},{"key":"ref231","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9014056"},{"key":"ref239","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM42002.2020.9322470"},{"key":"ref238","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2020.102152"},{"key":"ref235","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2019.2933807"},{"key":"ref234","doi-asserted-by":"publisher","DOI":"10.1145\/3229762.3229765"},{"key":"ref237","doi-asserted-by":"publisher","DOI":"10.23919\/DATE48585.2020.9116528"},{"key":"ref236","doi-asserted-by":"publisher","DOI":"10.1109\/HSI49210.2020.9142631"},{"key":"ref136","first-page":"854","article-title":"Distributed exploration in multi-armed bandits","author":"hillel","year":"2013","journal-title":"Proc Int Conf Adv Neural Inf Process Syst"},{"key":"ref135","first-page":"260","article-title":"Distributed non-stochastic experts","author":"kanade","year":"2012","journal-title":"Proc Int Conf Adv Neural Inf Process Syst"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2018.2852361"},{"key":"ref137","first-page":"19","article-title":"Gossip-based distributed stochastic bandit algorithms","volume":"28","author":"szorenyi","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2016.7798264"},{"key":"ref140","first-page":"4529","article-title":"Decentralized cooperative stochastic bandits","author":"martinez-rubio","year":"2019","journal-title":"Proc Int Conf Adv Neural Inf Process Syst"},{"key":"ref141","first-page":"4120","article-title":"Optimal algorithms for multiplayer multi-armed bandits","author":"wang","year":"2020","journal-title":"Proc Int Conf Artif Intell Stat"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1145\/3366701"},{"key":"ref143","first-page":"3471","article-title":"The gossiping insert-eliminate algorithm for multi-agent bandits","author":"chawla","year":"2020","journal-title":"Proc Int Conf Artif Intell Stat"},{"key":"ref2","article-title":"Deep speech: Scaling up end-to-end speech recognition","author":"hannun","year":"2014","journal-title":"arXiv 1412 5567"},{"key":"ref144","article-title":"Multi-agent multi-armed bandits with limited communication","author":"agarwal","year":"2021","journal-title":"arXiv 2102 08462"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref145","first-page":"1","article-title":"Distributed bandit learning: Near-optimal regret with efficient communication","author":"wang","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref241","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOMWKSHPS47286.2019.9093759"},{"key":"ref242","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-021-09987-4"},{"key":"ref243","first-page":"1","article-title":"Understanding the power consumption of executing deep neural networks on a distributed robot system","author":"hadidi","year":"2019","journal-title":"Proc CODES+ISSS Int Conf Hardw Softw Codesign Syst Synth"},{"key":"ref244","article-title":"Beyond inferring class representatives: User-level privacy leakage from federated learning","author":"wang","year":"2018","journal-title":"arXiv 1812 00535"},{"key":"ref240","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3016694"},{"key":"ref248","doi-asserted-by":"publisher","DOI":"10.23919\/ACC50511.2021.9483080"},{"key":"ref247","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref246","doi-asserted-by":"publisher","DOI":"10.1504\/IJSN.2015.071829"},{"key":"ref245","first-page":"1846","article-title":"Free-rider attacks on model aggregation in federated learning","author":"fraboni","year":"2021","journal-title":"Proc AISTATS"},{"key":"ref249","doi-asserted-by":"publisher","DOI":"10.1109\/SPW50608.2020.00027"},{"key":"ref109","article-title":"User selection approaches to mitigate the straggler effect for federated learning on cell-free massive MIMO networks","author":"vu","year":"2020","journal-title":"arXiv 2009 02031"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops49005.2020.9145182"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737464"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref103","article-title":"On the convergence of FedAvg on non-IID data","author":"li","year":"2019","journal-title":"arXiv 1907 02189"},{"key":"ref102","article-title":"Federated learning with non-IID data","author":"zhao","year":"2018","journal-title":"arXiv 1806 00582"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2956615"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1045"},{"key":"ref250","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref251","article-title":"Differentially private federated learning: A client level perspective","author":"geyer","year":"2017","journal-title":"arXiv 1712 07557"},{"key":"ref254","article-title":"Differential privacy-enabled federated learning for sensitive health data","author":"choudhury","year":"2020","journal-title":"arXiv 1910 02578"},{"key":"ref255","doi-asserted-by":"publisher","DOI":"10.1109\/DCC.2019.00101"},{"key":"ref252","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref253","article-title":"Differentially-private federated linear bandits","author":"dubey","year":"2020","journal-title":"arXiv 2010 11425"},{"key":"ref257","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761267"},{"key":"ref256","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2787987"},{"key":"ref259","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2019.2921755"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2017.9"},{"key":"ref258","doi-asserted-by":"publisher","DOI":"10.1109\/BigDataCongress.2017.85"},{"key":"ref11","author":"swinhoe","year":"2021","journal-title":"The 15 Biggest Data Breaches of the 21st Century"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800083"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-819045-6.00003-0"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/1279540.1279553"},{"key":"ref16","first-page":"19","author":"colace","year":"2015","journal-title":"Pervasive Systems Architecture and the Main Related Technologies"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2017.04.012"},{"key":"ref117","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761591"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.06.038"},{"key":"ref119","article-title":"Speech commands: A dataset for limited-vocabulary speech recognition","author":"warden","year":"2018","journal-title":"arXiv 1804 03209"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3040867"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3053588"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3009406"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2017.01.083"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2012.08.009"},{"key":"ref122","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017","journal-title":"ArXiv 1708 07747"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148776"},{"key":"ref260","article-title":"Practical secure aggregation for federated learning on user-held data","author":"bonawitz","year":"2016","journal-title":"arXiv 1611 04482"},{"key":"ref261","article-title":"Security of distributed machine learning: A game-theoretic approach to design secure DSVM","author":"zhang","year":"2020","journal-title":"arXiv 2003 04735"},{"key":"ref262","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2802721"},{"key":"ref263","article-title":"Augmented Lagrangian and alternating direction methods for convex optimization: A tutorial and some illustrative computational results","author":"eckstein","year":"2012"},{"key":"ref264","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2015.7447103"},{"key":"ref265","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref266","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2993966"}],"container-title":["IEEE Communications Surveys &amp; Tutorials"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9739\/9956928\/09866918.pdf?arnumber=9866918","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T19:18:22Z","timestamp":1670872702000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9866918\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":299,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/comst.2022.3200740","relation":{},"ISSN":["1553-877X","2373-745X"],"issn-type":[{"value":"1553-877X","type":"electronic"},{"value":"2373-745X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}