{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T13:09:44Z","timestamp":1751288984848,"version":"3.37.3"},"reference-count":99,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"European Union NextGeneration EU","award":["PNRR Mis. 4 Comp. 2 Inv. 1.3 D.D. 1555 11\/10\/2022"],"award-info":[{"award-number":["PNRR Mis. 4 Comp. 2 Inv. 1.3 D.D. 1555 11\/10\/2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3387453","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T19:05:26Z","timestamp":1712862326000},"page":"57043-57058","source":"Crossref","is-referenced-by-count":2,"title":["Accelerating Federated Learning via Sequential Training of Grouped Heterogeneous Clients"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2104-4576","authenticated-orcid":false,"given":"Andrea","family":"Silvi","sequence":"first","affiliation":[{"name":"Dipartimento di Automatica e Informatica (DAUIN), Politecnico di Torino, Turin, Italy"}]},{"given":"Andrea","family":"Rizzardi","sequence":"additional","affiliation":[{"name":"Dipartimento di Automatica e Informatica (DAUIN), Politecnico di Torino, Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8328-8622","authenticated-orcid":false,"given":"Debora","family":"Caldarola","sequence":"additional","affiliation":[{"name":"Dipartimento di Automatica e Informatica (DAUIN), Politecnico di Torino, Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7169-0158","authenticated-orcid":false,"given":"Barbara","family":"Caputo","sequence":"additional","affiliation":[{"name":"Dipartimento di Automatica e Informatica (DAUIN), Politecnico di Torino, Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3306-1323","authenticated-orcid":false,"given":"Marco","family":"Ciccone","sequence":"additional","affiliation":[{"name":"Dipartimento di Automatica e Informatica (DAUIN), Politecnico di Torino, Turin, Italy"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","volume":"54","author":"McMahan"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1109\/MSP.2020.2975749"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1007\/978-3-031-20050-2_38"},{"key":"ref4","article-title":"Federated learning for mobile keyboard prediction","author":"Hard","year":"2018","journal-title":"arXiv:1811.03604"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1007\/978-3-030-63076-8_16"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1007\/978-3-030-58607-2_5"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1109\/IROS47612.2022.9981098"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/WACV56688.2023.00052"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref10","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref11","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018","journal-title":"arXiv:1806.00582"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref13","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","volume":"119","author":"Karimireddy"},{"key":"ref14","article-title":"Understanding and improving model averaging in federated learning on heterogeneous data","author":"Zhou","year":"2023","journal-title":"arXiv:2305.07845"},{"volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Acar","article-title":"Federated learning based on dynamic regularization","key":"ref15"},{"key":"ref16","first-page":"18250","article-title":"Generalized federated learning via sharpness aware minimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Qu"},{"volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Sun","article-title":"FedSpeed: Larger local interval, less communication round, and higher generalization accuracy","key":"ref17"},{"key":"ref18","article-title":"Federated multitask learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Smith"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1109\/TNNLS.2022.3152581"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1109\/TNNLS.2022.3224252"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref22","article-title":"FedFMC: Sequential efficient federated learning on non-iid data","author":"Kopparapu","year":"2020","journal-title":"arXiv:2006.10937"},{"doi-asserted-by":"publisher","key":"ref23","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1007\/s11280-022-01046-x"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1109\/CVPRW53098.2021.00309"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/TNNLS.2023.3264740"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1561\/9781680837896"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1109\/ICPR56361.2022.9956084"},{"doi-asserted-by":"publisher","key":"ref29","DOI":"10.1109\/ICCV.2019.00653"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.1007\/978-3-031-00126-0_34"},{"doi-asserted-by":"publisher","key":"ref31","DOI":"10.1007\/978-3-030-63076-8_1"},{"doi-asserted-by":"publisher","key":"ref32","DOI":"10.1109\/TNNLS.2022.3216981"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.1109\/TNNLS.2022.3169347"},{"doi-asserted-by":"publisher","key":"ref34","DOI":"10.1145\/3128572.3140451"},{"doi-asserted-by":"publisher","key":"ref35","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"ref36","first-page":"16937","article-title":"Inverting gradients-how easy is it to break privacy in federated learning?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Geiping"},{"key":"ref37","first-page":"29898","article-title":"Gradient inversion with generative image prior","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Jeon"},{"doi-asserted-by":"publisher","key":"ref38","DOI":"10.1007\/s41666-020-00082-4"},{"volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li","article-title":"On the convergence of FedAvg on non-iid data","key":"ref39"},{"volume-title":"Proc. Adv. Neural Inf. Process. Syst. Workshop","author":"Hsu","article-title":"Measuring the effects of nonidentical data distribution for federated visual classification","key":"ref40"},{"doi-asserted-by":"publisher","key":"ref41","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref42","article-title":"First analysis of local GD on heterogeneous data","author":"Khaled","year":"2019","journal-title":"arXiv:1909.04715"},{"doi-asserted-by":"publisher","key":"ref43","DOI":"10.1109\/CVPR52688.2022.00987"},{"doi-asserted-by":"publisher","key":"ref44","DOI":"10.1109\/CVPR52688.2022.00821"},{"doi-asserted-by":"publisher","key":"ref45","DOI":"10.1109\/CISS53076.2022.9751166"},{"doi-asserted-by":"publisher","key":"ref46","DOI":"10.1109\/ICDE53745.2022.00238"},{"doi-asserted-by":"publisher","key":"ref47","DOI":"10.1109\/CDC51059.2022.9992745"},{"key":"ref48","first-page":"32991","article-title":"Dynamic regularized sharpness aware minimization in federated learning: Approaching global consistency and smooth landscape","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sun"},{"volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Reddi","article-title":"Adaptive federated optimization","key":"ref49"},{"key":"ref50","first-page":"22802","article-title":"Communication-efficient adaptive federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wang"},{"volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Karimireddy","article-title":"Mime: Mimicking centralized stochastic algorithms in federated learning","key":"ref51"},{"volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wang","article-title":"SlowMo: Improving communication-efficient distributed sgd with slow momentum","key":"ref52"},{"doi-asserted-by":"publisher","key":"ref53","DOI":"10.1109\/ISIT45174.2021.9517850"},{"key":"ref54","article-title":"FedCM: Federated learning with client-level momentum","author":"Xu","year":"2021","journal-title":"arXiv:2106.10874"},{"key":"ref55","first-page":"496","article-title":"Faster non-convex federated learning via global and local momentum","volume-title":"Proc. Uncertainty Artif. Intell.","author":"Das"},{"key":"ref56","article-title":"Communication-efficient federated learning with accelerated client gradient","author":"Kim","year":"2022","journal-title":"arXiv:2201.03172"},{"doi-asserted-by":"publisher","key":"ref57","DOI":"10.1109\/ICCVW60793.2023.00240"},{"volume-title":"Proc. Adv. Neural Inf. Process. Syst. Workshop","author":"Li","article-title":"FedMD: Heterogenous federated learning via model distillation","key":"ref58"},{"doi-asserted-by":"publisher","key":"ref59","DOI":"10.1109\/CVPR52688.2022.00990"},{"doi-asserted-by":"publisher","key":"ref60","DOI":"10.1023\/A:1007379606734"},{"volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fallah","article-title":"Personalized federated learning: A meta-learning approach","key":"ref61"},{"doi-asserted-by":"publisher","key":"ref62","DOI":"10.1109\/tnnls.2022.3213777"},{"doi-asserted-by":"publisher","key":"ref63","DOI":"10.1037\/met0000301"},{"issue":"6","key":"ref64","first-page":"7705","article-title":"Comparative analysis of anti-clusters formed using various distance metrics and k-medoids algorithm","volume":"29","author":"Fayaz","year":"2020","journal-title":"Int. J. Adv. Sci. Technol."},{"doi-asserted-by":"publisher","key":"ref65","DOI":"10.1007\/BFb0033314"},{"key":"ref66","article-title":"BrainTorrent: A peer-to-peer environment for decentralized federated learning","author":"Roy","year":"2019","journal-title":"arXiv:1905.06731"},{"key":"ref67","article-title":"Decentralized federated learning: A segmented gossip approach","author":"Hu","year":"2019","journal-title":"arXiv:1908.07782"},{"doi-asserted-by":"publisher","key":"ref68","DOI":"10.1109\/WACV56688.2023.00647"},{"key":"ref69","article-title":"Asynchronous federated optimization","author":"Xie","year":"2019","journal-title":"arXiv:1903.03934"},{"doi-asserted-by":"publisher","key":"ref70","DOI":"10.1016\/j.cosrev.2023.100595"},{"doi-asserted-by":"publisher","key":"ref71","DOI":"10.1109\/TNNLS.2019.2953131"},{"doi-asserted-by":"publisher","key":"ref72","DOI":"10.1109\/ACCESS.2020.2978082"},{"doi-asserted-by":"publisher","key":"ref73","DOI":"10.1109\/ICPADS51040.2020.00030"},{"doi-asserted-by":"publisher","key":"ref74","DOI":"10.1109\/EuroSP57164.2023.00020"},{"doi-asserted-by":"publisher","key":"ref75","DOI":"10.1109\/EuroSP57164.2023.00023"},{"doi-asserted-by":"publisher","key":"ref76","DOI":"10.1145\/3133956.3134012"},{"doi-asserted-by":"publisher","key":"ref77","DOI":"10.5555\/2969033.2969125"},{"doi-asserted-by":"publisher","key":"ref78","DOI":"10.1109\/CVPR52688.2022.00989"},{"doi-asserted-by":"publisher","key":"ref79","DOI":"10.1109\/SP.2019.00029"},{"key":"ref80","article-title":"Fishing for user data in large-batch federated learning via gradient magnification","author":"Wen","year":"2022","journal-title":"arXiv:2202.00580"},{"doi-asserted-by":"publisher","key":"ref81","DOI":"10.1007\/11787006_1"},{"doi-asserted-by":"publisher","key":"ref82","DOI":"10.1109\/TIFS.2020.2988575"},{"doi-asserted-by":"publisher","key":"ref83","DOI":"10.1109\/TNNLS.2022.3212627"},{"doi-asserted-by":"publisher","key":"ref84","DOI":"10.1145\/322077.322090"},{"key":"ref85","first-page":"5972","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non- IID data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Luo"},{"doi-asserted-by":"publisher","key":"ref86","DOI":"10.1109\/TKDE.2008.239"},{"doi-asserted-by":"publisher","key":"ref87","DOI":"10.1348\/000711005X48266"},{"doi-asserted-by":"publisher","key":"ref88","DOI":"10.1073\/pnas.1611835114"},{"year":"2009","author":"Krizhevsky","article-title":"Learning multiple layers of features from tiny images","key":"ref89"},{"volume-title":"Proc. Workshop Federated Learn. Data Privacy Confidentiality","author":"Caldas","article-title":"LEAF: A benchmark for federated settings","key":"ref90"},{"volume-title":"Tensorflow Federated Stack Overflow Dataset","year":"2019","key":"ref91"},{"doi-asserted-by":"publisher","key":"ref92","DOI":"10.1007\/978-3-031-20050-2_41"},{"doi-asserted-by":"publisher","key":"ref93","DOI":"10.1109\/TKDE.2021.3124599"},{"doi-asserted-by":"publisher","key":"ref94","DOI":"10.1126\/science.153.3731.34"},{"doi-asserted-by":"publisher","key":"ref95","DOI":"10.1080\/14786440109462720"},{"issue":"8","key":"ref96","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"doi-asserted-by":"publisher","key":"ref97","DOI":"10.1145\/3422622"},{"volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Heusel","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","key":"ref98"},{"doi-asserted-by":"publisher","key":"ref99","DOI":"10.1109\/ICIVC.2017.7984661"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10497106.pdf?arnumber=10497106","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T19:10:14Z","timestamp":1714763414000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10497106\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":99,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3387453","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2024]]}}}