{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T22:28:01Z","timestamp":1769639281695,"version":"3.49.0"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Swedish Foundation for Strategic Research","award":["ID17-0114"],"award-info":[{"award-number":["ID17-0114"]}]},{"name":"European Union's Horizon Europe research and innovation programme","award":["101067652"],"award-info":[{"award-number":["101067652"]}]},{"name":"Swedish SSF project SAICOM","award":["FUS21-0004"],"award-info":[{"award-number":["FUS21-0004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Signal Process."],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1109\/jstsp.2022.3221681","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T21:48:27Z","timestamp":1668462507000},"page":"205-221","source":"Crossref","is-referenced-by-count":6,"title":["Federated Learning Using Three-Operator ADMM"],"prefix":"10.1109","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5334-4734","authenticated-orcid":false,"given":"Shashi","family":"Kant","sequence":"first","affiliation":[{"name":"Ericsson AB and KTH Royal Institute of Technology, Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4503-4242","authenticated-orcid":false,"given":"Jose Mairton B. da","family":"Silva","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology, Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2289-3159","authenticated-orcid":false,"given":"Gabor","family":"Fodor","sequence":"additional","affiliation":[{"name":"Ericsson AB and KTH Royal Institute of Technology, Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7882-3280","authenticated-orcid":false,"given":"Bo","family":"Goransson","sequence":"additional","affiliation":[{"name":"Ericsson AB and KTH Royal Institute of Technology, Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3599-5584","authenticated-orcid":false,"given":"Mats","family":"Bengtsson","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology, Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9810-3478","authenticated-orcid":false,"given":"Carlo","family":"Fischione","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology, Stockholm, Sweden"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Federated learning: Strategies for improving communication efficiency","volume-title":"Proc. Neural Inf. Process. Syst. Private Multi-Party Mach. Learn. Workshop","author":"Konen\u1ef3","year":"2016"},{"key":"ref2","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan","year":"2017"},{"key":"ref3","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Li","year":"2020"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-93736-2_14"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9467-7"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3068207"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1561\/9781638280071"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3243080"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2018.1429274"},{"key":"ref10","first-page":"3368","article-title":"Gradient coding: Avoiding stragglers in distributed learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tandon","year":"2017"},{"key":"ref11","first-page":"5050","article-title":"LAG: Lazily aggregated gradient for communication-efficient distributed learning","volume":"31","author":"Chen","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref13","first-page":"15453","article-title":"Differential privacy has disparate impact on model accuracy","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bagdasaryan","year":"2019"},{"key":"ref14","first-page":"4447","article-title":"Sparsified SGD with memory","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Stich","year":"2018"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2018.2859932"},{"key":"ref16","first-page":"1299","article-title":"Gradient sparsification for communication-efficient distributed optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wangni","year":"2018"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref18","first-page":"3370","article-title":"Communication-efficient distributed learning via lazily aggregated quantized gradients","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sun","year":"2019"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015693"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9569-8_10"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1561\/9781601987174"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2377273"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41589-5"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974997"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s11228-017-0421-z"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s10915-018-0680-3"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1017\/9781009160865"},{"key":"ref29","first-page":"7057","article-title":"FedSplit: An algorithmic framework for fast federated optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Pathak","year":"2020"},{"key":"ref30","first-page":"4447","article-title":"FedDRrandomized Douglas-Rachford splitting algorithms for nonconvex federated composite optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Tran-Dinh","year":"2021"},{"key":"ref31","article-title":"Communication-efficient ADMM-based federated learning","author":"Zhou","year":"2022"},{"key":"ref32","first-page":"2816","article-title":"Bregman alternating direction method of multipliers","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang","year":"2014"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s11075-020-00934-5"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3069677"},{"key":"ref35","article-title":"Proximal splitting algorithms: A tour of recent advances, with new twists!","author":"Condat","year":"2021"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-016-1034-2"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3177136"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/BF02612715"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1561\/2600000008"},{"key":"ref40","article-title":"An introduction to three-operator ADMM for wireless communications and machine learning","author":"Kant","year":"2022"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref42","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511921490"}],"container-title":["IEEE Journal of Selected Topics in Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4200690\/10050192\/09947287.pdf?arnumber=9947287","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T02:15:24Z","timestamp":1706753724000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9947287\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":44,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/jstsp.2022.3221681","relation":{},"ISSN":["1932-4553","1941-0484"],"issn-type":[{"value":"1932-4553","type":"print"},{"value":"1941-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]}}}