{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T17:07:32Z","timestamp":1781284052110,"version":"3.54.1"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,17]]},"DOI":"10.1109\/bigdata55660.2022.10020641","type":"proceedings-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T19:35:23Z","timestamp":1674761723000},"page":"85-94","source":"Crossref","is-referenced-by-count":3,"title":["Aggregation Delayed Federated Learning"],"prefix":"10.1109","author":[{"given":"Ye","family":"Xue","sequence":"first","affiliation":[{"name":"Northwestern University,Evanston,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego","family":"Klabjan","sequence":"additional","affiliation":[{"name":"Northwestern University,Evanston,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Northwestern University,Chicago,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref2","article-title":"Federated learning for emoji prediction in a mobile keyboard","author":"Ramaswamy","year":"2019"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2021.3058627"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103291"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0230706"},{"key":"ref6","author":"Zhao","year":"2018","journal-title":"Federated learning with non-iid data"},{"key":"ref7","article-title":"Gradient scheduling with global momentum for non-iid data distributed asynchronous training","author":"Li","year":"2019"},{"key":"ref8","author":"Xie","year":"2020","journal-title":"Asynchronous federated optimization"},{"key":"ref9","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine Learning and Systems","author":"Li"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref11","first-page":"561","article-title":"Ray: A distributed framework for emerging ai applications","volume-title":"13th USENIX Symposium on Operating Systems Design and Implementation","author":"Moritz"},{"key":"ref12","author":"Kone\u010dn\u1ef3","year":"2017","journal-title":"Federated learning: Strategies for improving communication efficiency"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00099"},{"key":"ref14","author":"Jeong","year":"2018","journal-title":"Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data"},{"key":"ref15","author":"Yao","year":"2021","journal-title":"Local-global knowledge distillation in heterogeneous federated learning with non-iid data"},{"key":"ref16","article-title":"Ensemble distillation for robust model fusion in federated learning","volume-title":"Advances in Neural Information Processing Systems 33","author":"Lin"},{"key":"ref17","author":"Li","year":"2019","journal-title":"Fedmd: Heterogenous federated learning via model distillation"},{"key":"ref18","first-page":"5132","article-title":"SCAFFOLD: stochastic controlled averaging for federated learning","volume-title":"Virtual Event, ser. Proceedings of Machine Learning Research","volume":"119","author":"Karimireddy"},{"key":"ref19","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume":"33","author":"Wang","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref20","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","volume-title":"CoRR","volume":"1909.06335","author":"Hsu"},{"key":"ref21","article-title":"Adaptive federated optimization","volume-title":"9th International Conference on Learning Representations","author":"Reddi"},{"key":"ref22","first-page":"2525","article-title":"Not all samples are created equal: Deep learning with importance sampling","volume-title":"International conference on machine learning","author":"Katharopoulos"},{"key":"ref23","article-title":"Federated learning under importance sampling","volume-title":"CoRR","volume":"abs\/2012.07383","author":"Rizk","year":"2020"},{"key":"ref24","volume-title":"Uci kdd archive","author":"Hettich","year":"1999"},{"key":"ref25","article-title":"Mnist handwritten digit database","volume-title":"ATT Labs","volume":"2","author":"LeCun","year":"2010"},{"key":"ref26","article-title":"Learning multiple layers of features from tiny images","volume-title":"University of Toronto, Technical Report","author":"Krizhevsky","year":"2009"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.178"},{"key":"ref29","author":"Ghosh","year":"2019","journal-title":"Robust federated learning in a heterogeneous environment"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_16"},{"key":"ref31","volume-title":"On federated learning of deep networks from non-iid data: Parameter divergence and the effects of hyperparametric methods","author":"Kim","year":"2020"}],"event":{"name":"2022 IEEE International Conference on Big Data (Big Data)","location":"Osaka, Japan","start":{"date-parts":[[2022,12,17]]},"end":{"date-parts":[[2022,12,20]]}},"container-title":["2022 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10020192\/10020156\/10020641.pdf?arnumber=10020641","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T06:02:13Z","timestamp":1707804133000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10020641\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,17]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/bigdata55660.2022.10020641","relation":{},"subject":[],"published":{"date-parts":[[2022,12,17]]}}}