{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T11:41:59Z","timestamp":1782992519052,"version":"3.54.5"},"reference-count":34,"publisher":"Informa UK Limited","issue":"1","funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62072247"],"award-info":[{"award-number":["62072247"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"High-level Personnel Project Funding of Jiangsu Province","award":["JSSCBS20210274"],"award-info":[{"award-number":["JSSCBS20210274"]}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of Experimental &amp; Theoretical Artificial Intelligence"],"published-print":{"date-parts":[[2024,1,2]]},"DOI":"10.1080\/0952813x.2022.2079730","type":"journal-article","created":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T07:51:55Z","timestamp":1653637915000},"page":"47-69","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":18,"title":["AFedAvg: communication-efficient federated learning aggregation with adaptive communication frequency and gradient sparse"],"prefix":"10.1080","volume":"36","author":[{"given":"Yanbin","family":"Li","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziming","family":"He","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingjian","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanliang","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shougang","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"301","published-online":{"date-parts":[[2022,5,27]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124312"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1045"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3024629"},{"key":"e_1_3_3_5_1","first-page":"1223","volume-title":"Advances in neural information processing systems","author":"Dean J.","year":"2012","unstructured":"Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., and Le, Q. V. (2012). Large scale distributed deep networks. In F. Pereira and C.J. Burges and L. Bottou and K.Q. Weinberger (Eds.), Advances in neural information processing systems (pp. 1223\u20131231) Curran Associates, Inc."},{"key":"e_1_3_3_6_1","first-page":"165","article-title":"Optimal distributed online prediction using mini-batches","volume":"13","author":"Dekel O.","year":"2012","unstructured":"Dekel, O., Gilad-Bachrach, R., Shamir, O., & Xiao, L. (2012, January). Optimal distributed online prediction using mini-batches. Journal of Machine Learning Research, 13(6), 165\u2013202. https:\/\/jmlr.org\/papers\/volume13\/dekel12a\/dekel12a.pdf","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2018.1552316"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLHPC.2016.004"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.2971418"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_12_1","first-page":"2525","volume-title":"Advances in neural information processing systems","author":"Jiang P.","year":"2018","unstructured":"Jiang, P., & Agrawal, G. (2018). A linear speedup analysis of distributed deep learning with sparse and quantized communication. In Advances in neural information processing systems (pp. 2525\u20132536). Curran Associates, Inc."},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1080\/09528139108915277"},{"key":"e_1_3_3_14_1","first-page":"3478","volume-title":"International conference on machine learning","author":"Koloskova A.","year":"2019","unstructured":"Koloskova, A., Stich, S., & Jaggi, M. (2019). Decentralized stochastic optimization and gossip algorithms with compressed communication. In International conference on machine learning (pp. 3478\u20133487)."},{"issue":"4","key":"e_1_3_3_15_1","article-title":"Learning multiple layers of features from tiny images","volume":"1","author":"Krizhevsky A.","year":"2009","unstructured":"Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images. Handbook of Systemic Autoimmune Diseases, 1(4). http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?","journal-title":"Handbook of Systemic Autoimmune Diseases"},{"key":"e_1_3_3_16_1","first-page":"1097","volume-title":"Advances in neural information processing systems","author":"Krizhevsky A.","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097\u20131105). Curran Associates, Inc."},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639343"},{"key":"e_1_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_3_19_1","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li T.","year":"2020","unstructured":"Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020a). Federated optimization in heterogeneous networks. In I. Dhillon and D. Papailiopoulos V. Sze (Eds.), Proceedings of Machine Learning and Systems, 2, 429\u2013450. https:\/\/proceedings.mlsys.org\/paper\/2020\/file\/38af86134b65d0f10fe33d30dd76442e-Paper.pdf","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2018.1508814"},{"key":"e_1_3_3_24_1","first-page":"1273","volume-title":"International conference on artificial intelligence and statistics","author":"Mcmahan H. B.","year":"2017","unstructured":"Mcmahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-Efficient learning of deep networks from decentralized data. In International conference on artificial intelligence and statistics, 1273\u20131282."},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2919736"},{"key":"e_1_3_3_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2014-274"},{"key":"e_1_3_3_30_1","first-page":"4424","volume-title":"Advances in neural information processing systems","author":"Smith V.","year":"2017","unstructured":"Smith, V., Chiang, C. K., Sanjabi, M., & Talwalkar, A. S. (2017). Federated multi-task learning. In I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan & R. Garnett (Eds.), Advances in neural information processing systems (pp. 4424\u20134434). Curran Associates, Inc."},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2015-354"},{"key":"e_1_3_3_33_1","first-page":"212","article-title":"Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD","volume":"1","author":"Wang J.","year":"2019","unstructured":"Wang, J., & Joshi, G. (2019). Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD. In A. Talwalkar and V. Smith M. Zaharia (Eds.), Proceedings of Machine Learning and Systems, 1, 212\u2013229. https:\/\/proceedings.mlsys.org\/paper\/2019\/file\/c8ffe9a587b126f152ed3d89a146b445-Paper.pdf","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2019.1653382"},{"issue":"213","key":"e_1_3_3_35_1","first-page":"1","article-title":"Cooperative SGD: A unified framework for the design and analysis of local-update SGD algorithms","volume":"22","author":"Wang J.","year":"2021","unstructured":"Wang, J., & Joshi, G. (2021). Cooperative SGD: A unified framework for the design and analysis of local-update SGD algorithms. Journal of Machine Learning Research, 22(213), 1\u201350. http:\/\/jmlr.org\/papers\/v22\/20-147.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_36_1","first-page":"1509","volume-title":"Advances in neural information processing systems","author":"Wen W.","year":"2017","unstructured":"Wen, W., Xu, C., Yan, F., Wu, C., Wang, Y., Chen, Y., & Li, H. (2017). Terngrad: ternary gradients to reduce communication in distributed deep learning. In & R. Garnett (Eds.), Advances in neural information processing systems (pp. 1509\u20131519) Curran Associates, Inc."},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3029109"},{"key":"e_1_3_3_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3339474"},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2020.2984620"},{"key":"e_1_3_3_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3016694"},{"key":"e_1_3_3_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2949348"}],"container-title":["Journal of Experimental &amp; Theoretical Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/0952813X.2022.2079730","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:52Z","timestamp":1701993652000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/0952813X.2022.2079730"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,27]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,2]]}},"alternative-id":["10.1080\/0952813X.2022.2079730"],"URL":"https:\/\/doi.org\/10.1080\/0952813x.2022.2079730","relation":{},"ISSN":["0952-813X","1362-3079"],"issn-type":[{"value":"0952-813X","type":"print"},{"value":"1362-3079","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,27]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=teta20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=teta20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2020-06-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-11","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}