{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:49:40Z","timestamp":1784303380382,"version":"3.55.0"},"reference-count":28,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2024,5,1]]},"DOI":"10.1587\/transfun.2023map0002","type":"journal-article","created":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T22:12:35Z","timestamp":1705529555000},"page":"762-769","source":"Crossref","is-referenced-by-count":3,"title":["Distributed Event-Triggered Stochastic Gradient-Tracking for Nonconvex Optimization"],"prefix":"10.1587","volume":"E107.A","author":[{"given":"Daichi","family":"ISHIKAWA","sequence":"first","affiliation":[{"name":"Graduate School of Engineering, Osaka University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoki","family":"HAYASHI","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering Science, Osaka University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shigemasa","family":"TAKAI","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Osaka University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] K. Sakurama and T. Sugie, \u201cGeneralized coordination of multi-robot systems,\u201d Foundations and Trends in Systems and Control, vol.9, no.1, pp.1-170, 2021. 10.1561\/2600000025","DOI":"10.1561\/2600000025"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] A. Nedi\u0107 and A. Ozdaglar, \u201cDistributed subgradient methods for multi-agent optimization,\u201d IEEE Trans. Autom. Control, vol.54, no.1, pp.48-61, 2009. 10.1109\/TAC.2008.2009515","DOI":"10.1109\/TAC.2008.2009515"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] S.S. Ram, A. Nedi\u0107, and V.V. Veeravalli, \u201cDistributed stochastic subgradient projection algorithms for convex optimization,\u201d J. Optim. Theory Appl., vol.147, no.3, pp.516-545, 2010. 10.1007\/s10957-010-9737-7","DOI":"10.1007\/s10957-010-9737-7"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] G. Qu and N. Li, \u201cHarnessing smoothness to accelerate distributed optimization,\u201d Proc. 55th IEEE Conference on Decision and Control, pp.159-166, 2016. 10.1109\/CDC.2016.7798263","DOI":"10.1109\/CDC.2016.7798263"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] A. Nedi\u0107, A. Olshevsky, and M.G. Rabbat, \u201cNetwork topology and communication-computation tradeoffs in decentralized optimization,\u201d Proc. IEEE, vol.106, no.5, pp.953-976, 2018. 10.1109\/jproc.2018.2817461","DOI":"10.1109\/JPROC.2018.2817461"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] T. Yang, X. Yi, J. Wu, Y. Yuan, D. Wu, Z. Meng, Y. Hong, H. Wang, Z. Lin, and K.H. Johansson, \u201cA survey of distributed optimization,\u201d Annual Reviews in Control, vol.47, pp.278-305, 2019. 10.1016\/j.arcontrol.2019.05.006","DOI":"10.1016\/j.arcontrol.2019.05.006"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] R. Adachi and Y. Wakasa, \u201cDistributed filter using ADMM for optimal estimation over wireless sensor network,\u201d IEICE Trans. Fundamentals, vol.E105-A, no.11, pp.1458-1465, Nov. 2022. 10.1587\/transfun.2021kep0008","DOI":"10.1587\/transfun.2021KEP0008"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] N. Hayashi and K. Sakurama, \u201cCommunication-aware distributed rebalancing for cooperative car-sharing service,\u201d IET Control Theory & Applications, vol.17, no.7, pp.850-867, 2023. 10.1049\/cth2.12417","DOI":"10.1049\/cth2.12417"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] M. Zhu and S. Mart\u00ednez, \u201cAn approximate dual subgradient algorithm for multi-agent non-convex optimization,\u201d IEEE Trans. Autom. Control, vol.58, no.6, pp.1534-1539, 2013. 10.1109\/TAC.2012.2228038","DOI":"10.1109\/TAC.2012.2228038"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] P.D. Lorenzo and G. Scutari, \u201cNEXT: In-network nonconvex optimization,\u201d IEEE Trans. Signal Inf. Process. over Netw., vol.2, no.2, pp.120-136, 2016. 10.1109\/tsipn.2016.2524588","DOI":"10.1109\/TSIPN.2016.2524588"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] T. Tatarenko and B. Touri, \u201cNon-convex distributed optimization,\u201d IEEE Trans. Autom. Control, vol.62, no.8, pp.3744-3757, 2017. 10.1109\/TAC.2017.2648041","DOI":"10.1109\/TAC.2017.2648041"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] X. Jiang, X. Zeng, J. Sun, and J. Chen, \u201cDistributed proximal gradient algorithm for non-convex optimization over time-varying networks,\u201d IEEE Trans. Control Netw. Syst., vol.10, no.2, pp.1005-1017, 2022. 10.1109\/TCNS.2022.3213706","DOI":"10.1109\/TCNS.2022.3213706"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] S. Vlaski and A.H. Sayed, \u201cDistributed learning in non-convex environments \u2014 Part I: Agreement at a linear rate,\u201d IEEE Trans. Signal Process., vol.69, pp.1242-1256, 2021. 10.1109\/tsp.2021.3050858","DOI":"10.1109\/TSP.2021.3050858"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] S. Vlaski and A.H. Sayed, \u201cDistributed learning in non-convex environments \u2014 Part II: Polynomial escape from saddle-points,\u201d IEEE Trans. Signal Process., vol.69, pp.1257-1270, 2021. 10.1109\/tsp.2021.3050840","DOI":"10.1109\/TSP.2021.3050840"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] R. Xin, U.A. Khan, and S. Kar, \u201cAn improved convergence analysis for decentralized online stochastic non-convex optimization,\u201d IEEE Trans. Signal Process., vol.69, pp.1842-1858, 2021. 10.1109\/tsp.2021.3062553","DOI":"10.1109\/TSP.2021.3062553"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] J. Gao, X.-W. Liu, Y.-H. Dai, Y. Huang, and J. Gu, \u201cDistributed stochastic gradient tracking methods with momentum acceleration for non-convex optimization,\u201d Comput. Optim. Appl., vol.84, pp.531-572, 2022. 10.1007\/s10589-022-00432-5","DOI":"10.1007\/s10589-022-00432-5"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] Y. Kajiyama, N. Hayashi, and S. Takai, \u201cDistributed subgradient method with edge-based event-triggered communication,\u201d IEEE Trans. Autom. Control, vol.63, no.7, pp.2248-2255, 2018. 10.1109\/TAC.2018.2800760","DOI":"10.1109\/TAC.2018.2800760"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] S. Liu, L. Xie, and D.E. Quevedo, \u201cEvent-triggered quantized communication-based distributed convex optimization,\u201d IEEE Trans. Control Netw. Syst., vol.5, no.1, pp.167-178, 2018. 10.1109\/TCNS.2016.2585305","DOI":"10.1109\/TCNS.2016.2585305"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] K. Ishikawa, N. Hayashi, and S. Takai, \u201cConsensus-based distributed particle swarm optimization with event-triggered communication,\u201d IEICE Trans. Fundamentals, vol.E101-A, no.2, pp.338-344, Feb. 2018. 10.1587\/transfun.e101.a.338","DOI":"10.1587\/transfun.E101.A.338"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] N. Hayashi, T. Sugiura, Y. Kajiyama, and S. Takai, \u201cDistributed event-triggered algorithm for unconstrained convex optimization over weight-balanced directed networks,\u201d IET Control Theory & Applications, vol.14, no.2, pp.253-261, 2020. 10.1049\/iet-cta.2019.0377","DOI":"10.1049\/iet-cta.2019.0377"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] S. Ghosh, B. Aquino, and V. Gupta, \u201cEventGraD: Event-triggered communication in parallel machine learning,\u201d Neurocomputing, vol.483, pp.474-487, 2022. 10.1016\/j.neucom.2021.08.143","DOI":"10.1016\/j.neucom.2021.08.143"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] G. Carnevale, I. Notarnicola, L. Marconi, and G. Notarstefano, \u201cTriggered gradient tracking for asynchronous distributed optimization,\u201d Automatica, vol.147, p.110726, 2023. 10.48550\/arXiv.2203.02210","DOI":"10.1016\/j.automatica.2022.110726"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] C. Nowzari, E. Garcia, and J. Cort\u00e9s, \u201cEvent-triggered communication and control of networked systems for multi-agent consensus,\u201d Automatica, vol.105, pp.1-27, 2019. 10.1016\/j.automatica.2019.03.009","DOI":"10.1016\/j.automatica.2019.03.009"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] K. Kitamura, K. Kobayashi, and Y. Yamashita, \u201cLMI-based design of output feedback controllers with decentralized event-triggering,\u201d IEICE Trans. Fundamentals, vol.E105-A, no.5, pp.816-822, May 2022. 10.1587\/transfun.2021map0004","DOI":"10.1587\/transfun.2021MAP0004"},{"key":"25","unstructured":"[25] J. George and P. Gurram, \u201cDistributed deep learning with event-triggered communication,\u201d arXiv preprint arXiv:1909.05020, 2019. 10.48550\/arXiv.1909.05020"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] T. Adachi, N. Hayashi, and S. Takai, \u201cDistributed gradient descent method with edge-based event-driven communication for non-convex optimization,\u201d IET Control Theory & Applications, vol.15, no.12, pp.1588-1598, 2021. 10.1049\/cth2.12127","DOI":"10.1049\/cth2.12127"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] S. Mao, Z. Dong, W. Du, Y.-C. Tian, C. Liang, and Y. Tang, \u201cDistributed nonconvex event-triggered optimization over time-varying directed networks,\u201d IEEE Trans. Ind. Inf., vol.18, no.7, pp.4737-4748, 2022. 10.1109\/tii.2021.3103747","DOI":"10.1109\/TII.2021.3103747"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] L. Deng, \u201cThe MNIST database of handwritten digit images for machine learning research,\u201d IEEE Signal Process. Mag., vol.29, no.6, pp.141-142, 2012. 10.1109\/msp.2012.2211477","DOI":"10.1109\/MSP.2012.2211477"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E107.A\/5\/E107.A_2023MAP0002\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T03:21:50Z","timestamp":1714792910000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E107.A\/5\/E107.A_2023MAP0002\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,1]]},"references-count":28,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2023map0002","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,1]]},"article-number":"2023MAP0002"}}