{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:08:24Z","timestamp":1764842904062,"version":"3.28.0"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,6,9]],"date-time":"2024-06-09T00:00:00Z","timestamp":1717891200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,9]],"date-time":"2024-06-09T00:00:00Z","timestamp":1717891200000},"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":[[2024,6,9]]},"DOI":"10.1109\/icc51166.2024.10622224","type":"proceedings-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T15:34:42Z","timestamp":1724168082000},"page":"280-286","source":"Crossref","is-referenced-by-count":2,"title":["FedCore: Straggler-Free Federated Learning with Distributed Coresets"],"prefix":"10.1109","author":[{"given":"Hongpeng","family":"Guo","sequence":"first","affiliation":[{"name":"University of Illinois,Urbana Champaign"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haotian","family":"Gu","sequence":"additional","affiliation":[{"name":"University of California,Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Illinois,Urbana Champaign"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Illinois,Urbana Champaign"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eun Kyung","family":"Lee","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tamar","family":"Eilam","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deming","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Illinois,Urbana Champaign"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klara","family":"Nahrstedt","sequence":"additional","affiliation":[{"name":"University of Illinois,Urbana Champaign"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11723-8_9"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107318"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32692-0_16"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2991401"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3381006"},{"key":"ref6","first-page":"13","article-title":"Hybridalpha: An efficient approach for privacy-preserving federated learning","volume-title":"Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security","author":"Xu"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_17"},{"key":"ref8","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"journal-title":"Asynchronous federated optimization","year":"2019","author":"Xie","key":"ref9"},{"article-title":"On the convergence of FedA vg on non-iid data","volume-title":"International Conference on Learning Representations","author":"Li","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3466752.3480129"},{"key":"ref12","first-page":"19","article-title":"Oort: Efficient federated learning via guided participant selection","author":"Lai","year":"2021","journal-title":"Operating Systems Design and Implementation"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3528535.3565244"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"journal-title":"Protection against reconstruction and its applications in private federated learning","year":"2018","author":"Bhowmick","key":"ref15"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3216981"},{"key":"ref17","article-title":"Papaya: Practical, private, and scalable federated learning","author":"Huba","year":"2022","journal-title":"MLsys"},{"key":"ref18","article-title":"Federated learning with buffered asynchronous aggregation","author":"Nguyen","year":"2022","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"article-title":"Coresets for data-efficient training of machine learning models","volume-title":"International Conference on Machine Learning","author":"Mirzasoleiman","key":"ref20"},{"article-title":"Grad-match: Gradient matching based data subset selection for efficient deep model training","volume-title":"International Conference on Machine Learning","author":"Killamsetty","key":"ref21"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-12423-5_14"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/s21092921"},{"journal-title":"Active learning for convolutional neural networks: A core-set approach","year":"2017","author":"Sener","key":"ref24"},{"key":"ref25","article-title":"Super-samples from kernel herding","author":"Chen","year":"2010","journal-title":"UAI10"},{"article-title":"Active learning for convolutional neural networks: A coreset approach","volume-title":"International Conference on Learning Representations","author":"Sener","key":"ref26"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2018.00081"},{"journal-title":"An empirical study of example forgetting during deep neural network learning","year":"2018","author":"Toneva","key":"ref28"},{"key":"ref29","first-page":"209","article-title":"Coresets for nonparametric estimation-the case of dp-means","volume-title":"International Conference on Machine Learning","author":"Bachem"},{"key":"ref30","first-page":"20596","article-title":"Deep learning on a data diet: Finding important examples early in training","volume":"34","author":"Paul","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"journal-title":"Adversarial active learning for deep networks: a margin based approach","year":"2018","author":"Ducoffe","key":"ref31"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.51"},{"journal-title":"Asynchronous federated learning with reduced number of rounds and with differential privacy from less aggregated gaussian noise","year":"2020","author":"van Dijk","key":"ref33"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/icmla55696.2022.00121"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3476211"},{"key":"ref36","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine Learning and Systems","volume":"2","author":"Li"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF44664.2019.9049023"},{"key":"ref38","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":"ref39","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316801"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3055399.3055448"},{"key":"ref41","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"},{"journal-title":"Fedcore: Straggler-free federated learning with distributed coresets","year":"2024","author":"Guo","key":"ref42"},{"journal-title":"On the convergence of local descent methods in federated learning","year":"2019","author":"Haddadpour","key":"ref43"},{"journal-title":"Fedml: A research li-brary and benchmark for federated machine learning","year":"2020","author":"He","key":"ref44"}],"event":{"name":"ICC 2024 - IEEE International Conference on Communications","start":{"date-parts":[[2024,6,9]]},"location":"Denver, CO, USA","end":{"date-parts":[[2024,6,13]]}},"container-title":["ICC 2024 - IEEE International Conference on Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10622104\/10622158\/10622224.pdf?arnumber=10622224","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T19:53:39Z","timestamp":1724961219000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10622224\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,9]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/icc51166.2024.10622224","relation":{},"subject":[],"published":{"date-parts":[[2024,6,9]]}}}