{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:34:20Z","timestamp":1780054460367,"version":"3.54.0"},"reference-count":89,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Future Communications Research and Development Programme","award":["FCP-NTU-RG-2022-010"],"award-info":[{"award-number":["FCP-NTU-RG-2022-010"]}]},{"name":"Future Communications Research and Development Programme","award":["FCP-ASTAR-TG-2022-003"],"award-info":[{"award-number":["FCP-ASTAR-TG-2022-003"]}]},{"DOI":"10.13039\/501100001459","name":"Ministry of Education - Singapore","doi-asserted-by":"publisher","award":["RG87\/22"],"award-info":[{"award-number":["RG87\/22"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001459","name":"Ministry of Education - Singapore","doi-asserted-by":"publisher","award":["RG24\/24"],"award-info":[{"award-number":["RG24\/24"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NTU Centre for Computational Technologies in Finance"},{"name":"Fund-Industry Collaboration Projects","award":["I2301E0026"],"award-info":[{"award-number":["I2301E0026"]}]},{"name":"Guangdong Key Program","award":["2021QN02X166"],"award-info":[{"award-number":["2021QN02X166"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1109\/tmc.2025.3543295","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T18:27:04Z","timestamp":1739903224000},"page":"6293-6314","source":"Crossref","is-referenced-by-count":12,"title":["Dynamic Distributed Model Compression for Efficient Decentralized Federated Learning and Incentive Provisioning in Edge Computing Networks"],"prefix":"10.1109","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4430-5928","authenticated-orcid":false,"given":"Alia","family":"Asheralieva","sequence":"first","affiliation":[{"name":"Department of Computer Science, Loughborough University, Loughborough, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7442-7416","authenticated-orcid":false,"given":"Dusit","family":"Niyato","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4450-2251","authenticated-orcid":false,"given":"Xuetao","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2022.3218527"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2022.3153408"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2984887"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2970550"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3075439"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3030072"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3316615"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3315746"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2024.3407584"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3231363"},{"key":"ref12","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref13","first-page":"4541","article-title":"COLA: Decentralized Linear Learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"He"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3230938"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3178378"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3242710"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3262514"},{"key":"ref18","article-title":"A comprehensive survey of incentive mechanism for federated learning","author":"Zeng","year":"2021"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2022.3177522"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3096076"},{"key":"ref21","first-page":"3478","article-title":"Decentralized stochastic optimization and gossip algorithms with compressed communication","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Koloskova"},{"key":"ref22","first-page":"1","article-title":"Decentralized deep learning with arbitrary communication compression","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Koloskova"},{"key":"ref23","first-page":"1","article-title":"Robust and communication-efficient collaborative learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Reisizadeh"},{"key":"ref24","first-page":"31653","article-title":"BEER: Fast O(1\/T) rate for decentralized nonconvex optimization with communication compression","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhao"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2022.3180695"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2022.3145576"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2022.3151242"},{"key":"ref28","first-page":"2021","article-title":"A communication-efficient federated learning method with periodic averaging and quantization","volume-title":"Proc. Int. Conf. AI Statist.","author":"Reisizadeh"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2961673"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3175887"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3373460"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2023.3319160"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3306778"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2024.3354713"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3394170"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3139039"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3182876"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2023.3240767"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3376792"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511800481.009"},{"key":"ref41","first-page":"2044","article-title":"Convergence to Nash equilibrium and no-regret guarantee in (Markov) potential games","volume-title":"Proc. Int. Conf. AI Statist.","author":"Dong"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2927233"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3362912"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3160968"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/lra.2022.3160968"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3114749"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3062827"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.23919\/JCC.2021.01.007"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2017.2765345"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1006\/game.1996.0044"},{"key":"ref51","first-page":"4024","article-title":"Independent learning in constrained Markov potential games","volume-title":"Proc. Int. Conf. AI Statist.","author":"Jordan"},{"key":"ref52","first-page":"4699","article-title":"Provable policy gradient methods for average-reward Markov potential games","volume-title":"Proc. Int. Conf. AI Statist.","author":"Cheng"},{"key":"ref53","first-page":"1","article-title":"Provably fast convergence of independent natural policy gradient for Markov potential games","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sun"},{"key":"ref54","first-page":"28815","article-title":"Learning distributed and fair policies for network load balancing as Markov potential game","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yao"},{"key":"ref55","first-page":"184","article-title":"Policy gradient play with networked agents in Markov potential games","volume-title":"Proc. Learn. Dyn. Control Conf.","author":"Aydin"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CDC51059.2022.9992762"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3273914"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.01.130"},{"key":"ref59","first-page":"10533","article-title":"Dres-FL: Dropout-resilient secure federated learning for non-IID clients via secret data sharing","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shao"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3146448"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3136308"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.camwa.2004.05.005"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2009.2016247"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.874516"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2017.05.008"},{"key":"ref66","first-page":"4452\u20134444\u201363","article-title":"Sparsified SGD with memory","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Stich"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2008.927071"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.23919\/ECC.2007.7068829"},{"key":"ref69","first-page":"5686","article-title":"Consensus control for decentralized deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kong"},{"key":"ref70","volume-title":"A Course in Game Theory","author":"Osborne","year":"1994"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.36.1.48"},{"key":"ref72","article-title":"Note on the extreme value theorem","year":"2024"},{"key":"ref73","article-title":"Global convergence of multi-agent policy gradient in Markov potential games","author":"Leonardos","year":"2021"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1007\/BF00939252"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2016.2551693"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2936345"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-937X.2007.00427.x"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-77247-9_18"},{"key":"ref79","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"ref80","first-page":"1225","article-title":"Train faster, generalize better: Stability of stochastic gradient descent","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hardt"},{"key":"ref81","article-title":"OPNET network simulator","year":"2024"},{"key":"ref82","doi-asserted-by":"crossref","DOI":"10.1515\/9783110724509","volume-title":"An Introduction to the 5th Generation Mobile Networks","author":"Trick","year":"2021"},{"key":"ref83","article-title":"NR; sidelink relay adaptation protocol (SRAP) specification","year":"2022"},{"key":"ref84","article-title":"NR; NR and NG-RAN overall description; stage-2","year":"2021"},{"key":"ref85","article-title":"NR; physical layer; general description","year":"2023"},{"key":"ref86","article-title":"NR; medium access control (MAC) protocol specification","year":"2021"},{"key":"ref87","first-page":"8253","article-title":"FetchSGD: Communication-efficient federated learning with sketching","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rothchild"},{"key":"ref88","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2024"},{"key":"ref89","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Paszke"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7755\/11025553\/10891878.pdf?arnumber=10891878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T17:44:18Z","timestamp":1749231858000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10891878\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":89,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2025.3543295","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]}}}