{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:41:43Z","timestamp":1783183303934,"version":"3.54.6"},"reference-count":183,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2020-04707"],"award-info":[{"award-number":["RGPIN-2020-04707"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012376","name":"Universit\u00e9 du Qu\u00e9bec en Outaouais","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012376","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010340","name":"Lebanese American University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010340","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004070","name":"Khalifa University of Science, Technology and Research","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004070","id-type":"DOI","asserted-by":"crossref"}]},{"name":"European Union\u2019s Horizon 2020 Research and Innovation Program","award":["101016509 (Project CHARITY)"],"award-info":[{"award-number":["101016509 (Project CHARITY)"]}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland 6Genesis Project","doi-asserted-by":"publisher","award":["318927"],"award-info":[{"award-number":["318927"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland CSN Project","doi-asserted-by":"publisher","award":["311654"],"award-info":[{"award-number":["311654"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Commun. Surv. Tutorials"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/comst.2021.3058573","type":"journal-article","created":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T15:39:57Z","timestamp":1621611597000},"page":"1342-1397","source":"Crossref","is-referenced-by-count":440,"title":["Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3991-4673","authenticated-orcid":false,"given":"Omar Abdel","family":"Wahab","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9434-5322","authenticated-orcid":false,"given":"Azzam","family":"Mourad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9574-5384","authenticated-orcid":false,"given":"Hadi","family":"Otrok","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1119-1239","authenticated-orcid":false,"given":"Tarik","family":"Taleb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1900506"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1701148"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2799820"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2016.2535718"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700879"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1109\/LNET.2019.2947144"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2017.1800116"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2967772"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006327"},{"key":"ref168","first-page":"1","article-title":"A hybrid approach to privacy-preserving federated learning","author":"truex","year":"2019","journal-title":"Proc 12th ACM Workshop Artif Intell Security"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2019.2921755"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.23919\/JCC.2020.09.009"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.001.1900323"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2970550"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-43020-7_86"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2014.2312291"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/Trustcom.2015.357"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.icte.2017.03.004","article-title":"A survey on LPWA technology: LoRa and NB-IoT","volume":"3","author":"sinha","year":"2017","journal-title":"ICT Exp"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2705720"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2750180"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2018.1700202"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/VTS-APWCS.2019.8851649"},{"key":"ref180","article-title":"Toward an automated auction framework for wireless federated learning services market","author":"jiao","year":"2020","journal-title":"IEEE Trans Mobile Comput"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2017.1500371WC"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2012.010912.00081"},{"key":"ref28","first-page":"19","article-title":"Gossip-based distributed stochastic bandit algorithms","author":"szorenyi","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327492"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2971981"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/1067627.806586"},{"key":"ref20","first-page":"1","article-title":"On the convergence of fedavg on non-IID data","author":"li","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref22","first-page":"2021","article-title":"FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization","author":"reisizadeh","year":"2020","journal-title":"Proc ACM Int Conf Artif Intell Stat"},{"key":"ref21","first-page":"429","article-title":"Federated optimization in heterogeneous networks","author":"li","year":"2020","journal-title":"Proc 3rd Conf Mach Learn Syst (MLSys)"},{"key":"ref24","first-page":"1","article-title":"Federated learning with matched averaging","author":"wang","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref23","author":"so","year":"2020","journal-title":"Turbo-aggregate Breaking the quadratic aggregation barrier in secure federated learning"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2961673"},{"key":"ref26","author":"bonawitz","year":"2019","journal-title":"Towards Federated Learning at Scale System Design"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148853"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref50","author":"lim","year":"2020","journal-title":"Towards federated learning in UAV-enabled Internet of Vehicles A multi-dimensional contract-matching approach"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.3017281"},{"key":"ref154","author":"bagdasaryan","year":"2018","journal-title":"How to backdoor federated learning"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000286"},{"key":"ref156","first-page":"1","article-title":"Quantification of the leakage in federated learning","author":"li","year":"2019","journal-title":"Proc Workshop Feder Learn Data Privacy Confidentiality Conjunction NeurIPS"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2756025"},{"key":"ref150","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume":"97","author":"bhagoji","year":"2019","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref152","author":"li","year":"2020","journal-title":"Learning to detect malicious clients for robust federated learning"},{"key":"ref151","author":"fung","year":"2018","journal-title":"Mitigating sybils in federated learning poisoning"},{"key":"ref146","first-page":"1605","article-title":"Local model poisoning attacks to byzantine-robust federated learning","author":"fang","year":"2020","journal-title":"Proc Usenix Security Symp"},{"key":"ref147","author":"mu\u00f1oz-gonz\u00e1lez","year":"2019","journal-title":"Byzantine-robust federated machine learning through adaptive model averaging"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011544"},{"key":"ref149","first-page":"1","article-title":"Abnormal client behavior detection in federated learning","author":"li","year":"2019","journal-title":"Proc 2nd Int Workshop Feder Learn Data Privacy Confidentiality Conjunction NeurIPS (FL-NeurIPS)"},{"key":"ref59","author":"goetz","year":"2019","journal-title":"Active federated learning"},{"key":"ref58","first-page":"1","article-title":"Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data","author":"jeong","year":"2018","journal-title":"Proc 2nd Workshop Mach Learn Phone Consumer Devices (MLPCD 2) NeurIPS"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9149323"},{"key":"ref56","author":"zhao","year":"2018","journal-title":"Federated learning with non-IID data"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD46524.2019.00038"},{"key":"ref54","author":"ng","year":"2020","journal-title":"Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles"},{"key":"ref53","author":"zeng","year":"2020","journal-title":"Federated learning in the sky Joint power allocation and scheduling with UAV swarms"},{"key":"ref52","article-title":"Federated learning in the sky: Aerial-ground air quality sensing framework with UAV swarms","author":"liu","year":"2020","journal-title":"IEEE Internet of Things Journal"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2956472"},{"key":"ref167","first-page":"1","article-title":"Practical secure aggregation for federated learning on user-held data","author":"bonawitz","year":"2016","journal-title":"Proc NIPS Workshop Private Multiparty Mach Learn"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref165","author":"feng","year":"2020","journal-title":"Practical and bilateral privacy-preserving federated learning"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1145\/3338466.3358926"},{"key":"ref163","author":"hardy","year":"2017","journal-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"},{"key":"ref162","author":"han","year":"2019","journal-title":"Robust federated training via collaborative machine teaching using trusted instances"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2993966"},{"key":"ref160","author":"liu","year":"2019","journal-title":"Enhancing the privacy of federated learning with sketching"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2017.2694426"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2579198"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3030072"},{"key":"ref159","first-page":"1","article-title":"Private federated learning with domain adaptation","author":"peterson","year":"2019","journal-title":"Proc Workshop Feder Learn Data Privacy Confidentiality Conjunction NeurIPS"},{"key":"ref8","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":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/LNET.2020.2966976"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.2994015"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref158","first-page":"1","article-title":"Differentially private federated learning: A client level perspective","author":"geyer","year":"2017","journal-title":"Proc NIPS Workshop Mach Learn Phone Consumer Devices"},{"key":"ref9","author":"kone?n?","year":"2016","journal-title":"Federated learning Strategies for improving communication efficiency"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2973651"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.2979149"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.2966989"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2944169"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2919736"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8647927"},{"key":"ref44","first-page":"8866","article-title":"Hierarchical federated learning across heterogeneous cellular networks","author":"abad","year":"2020","journal-title":"Proc IEEE Int Conf Acoust Speech Signal Procesd (ICASSP)"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2968399"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006179"},{"key":"ref126","author":"he","year":"2019","journal-title":"Central server free federated learning over single-sided trust social networks"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.001.1900119"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2940820"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref72","author":"ruan","year":"2020","journal-title":"Towards flexible device participation in federated learning for non-IID data"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/90.993305"},{"key":"ref71","first-page":"1","article-title":"Robust federated learning through representation matching and adaptive hyper-parameters","author":"mostafa","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3028742"},{"key":"ref70","author":"chen","year":"2019","journal-title":"Distributed training with heterogeneous data Bridging median and mean based algorithms"},{"key":"ref76","first-page":"6","article-title":"Agnostic federated learning","author":"mohri","year":"2019","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2013.2262153"},{"key":"ref77","first-page":"1","article-title":"Fair resource allocation in federated learning","author":"li","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref74","first-page":"1764","article-title":"Semi-cyclic stochastic gradient descent","volume":"97","author":"eichner","year":"2019","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref75","first-page":"1","article-title":"FedMD: Heterogenous federated learning via model distillation","author":"li","year":"2019","journal-title":"Proc NeurIPS Workshop Feder Learn Data Privacy Confidentiality"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2712560"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2869226"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1002\/nav.3800020109"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1117\/12.2519621"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2016.7553036"},{"key":"ref79","first-page":"552","article-title":"Better mixing via deep representations","author":"bengio","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1137\/1035044"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2956615"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2015.04.009"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2016.2549019"},{"key":"ref60","first-page":"4424","article-title":"Federated multi-task learning","author":"smith","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref139","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/978-3-030-65310-1_23","article-title":"A trust and energy-aware double deep reinforcement learning scheduling strategy for federated learning on IoT devices","author":"rjoub","year":"2020","journal-title":"Proc Int Conf Service Orient Comput"},{"key":"ref62","author":"sattler","year":"2019","journal-title":"Clustered federated learning Model-agnostic distributed multi-task optimization under privacy constraints"},{"key":"ref61","first-page":"1","article-title":"Federated kernelized multi-task learning","author":"caldas","year":"2018","journal-title":"Proc SysML Conf"},{"key":"ref63","author":"ghosh","year":"2019","journal-title":"Robust federated learning in a heterogeneous environment"},{"key":"ref64","author":"briggs","year":"2020","journal-title":"Federated learning with hierarchical clustering of local updates to improve training on non-IID data"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511528446"},{"key":"ref65","author":"liu","year":"2018","journal-title":"Secure federated transfer learning"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS.2017.88"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006280"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.09.047"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.2975189"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2012.2223766"},{"key":"ref68","first-page":"5904","article-title":"Collaborative deep learning in fixed topology networks","author":"jiang","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.03.102"},{"key":"ref2","article-title":"Resource-aware detection and defense system against multi-type attacks in the cloud: Repeated Bayesian stackelberg game","author":"wahab","year":"2019","journal-title":"IEEE Trans Depend Secure Comput"},{"key":"ref69","author":"koskela","year":"2018","journal-title":"Learning rate adaptation for federated and differentially private learning"},{"key":"ref145","first-page":"119","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","author":"blanchard","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2015.2487344"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2583465"},{"key":"ref95","first-page":"1","article-title":"One-shot federated learning","author":"guha","year":"2018","journal-title":"Proc 2nd Workshop Mach Learn Phone Consumer Devices NeurIPs"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2008.4625804"},{"key":"ref94","author":"niu","year":"2019","journal-title":"Secure federated submodel learning"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2014.2375934"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2919699"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2000.5340728"},{"key":"ref92","first-page":"7252","article-title":"Bayesian nonparametric federated learning of neural networks","author":"yurochkin","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/SPAWC48557.2020.9154266"},{"key":"ref91","author":"caldas","year":"2018","journal-title":"Expanding the reach of federated learning by reducing client resource requirements"},{"key":"ref104","author":"chen","year":"2019","journal-title":"Asynchronous online federated learning for edge devices"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2974748"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.2994391"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.2981904"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2015.2437879"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2018.2877684"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2014.2323395"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800286"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00148"},{"key":"ref96","author":"liu","year":"2019","journal-title":"A communication efficient vertical federated learning framework"},{"key":"ref97","first-page":"393","article-title":"Efficient decentralized deep learning by dynamic model averaging","author":"kamp","year":"2018","journal-title":"Proc Eur Conf Mach Learn Knowl Discovery Databases"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref11","author":"kairouz","year":"2019","journal-title":"Advances and Open Problems in Federated Learning"},{"key":"ref12","author":"li","year":"2019","journal-title":"A survey on federated learning systems vision hype and reality for data privacy and protection"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900461"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref118","author":"chen","year":"2019","journal-title":"A joint learning and communications framework for federated learning over wireless networks"},{"key":"ref16","author":"xu","year":"2019","journal-title":"Federated Learning for Healthcare Informatics"},{"key":"ref82","author":"cheng","year":"2019","journal-title":"SecureBoost A lossless federated learning framework"},{"key":"ref117","first-page":"1","article-title":"Think locally, act globally: Federated learning with local and global representations","author":"liang","year":"2019","journal-title":"Proc Workshop Feder Learn NeurIPS"},{"key":"ref17","author":"gu","year":"2019","journal-title":"Distributed Machine Learning on Mobile Devices A Survey"},{"key":"ref81","author":"jing","year":"2019","journal-title":"Quantifying the performance of federated transfer learning"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2920930"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2013.103013.00206"},{"key":"ref119","first-page":"1","article-title":"Decentralized federated learning: A segmented Gossip approach","author":"hu","year":"2019","journal-title":"Proc 1st Int Workshop Feder Mach Learn User Privacy Data Confidentiality (FML)"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/PLANS46316.2020.9110155"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2871070"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2017.7996952"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2013.6704481"},{"key":"ref80","first-page":"1","article-title":"On the convergence of local descent methods in federated learning","author":"haddadpour","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.1997.635121"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2984332"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref121","author":"nguyen","year":"2019","journal-title":"Resource allocation in mobility-aware federated learning networks A deep reinforcement learning approach"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2019.2917133"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2015.2494502"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622525"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"},{"key":"ref88","first-page":"7564","article-title":"cpSGD: Communication-efficient and differentially-private distributed SGD","author":"agarwal","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Communications Surveys &amp; Tutorials"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9739\/9438976\/09352033.pdf?arnumber=9352033","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:52:27Z","timestamp":1652194347000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9352033\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":183,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/comst.2021.3058573","relation":{},"ISSN":["1553-877X","2373-745X"],"issn-type":[{"value":"1553-877X","type":"electronic"},{"value":"2373-745X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}