{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:04:34Z","timestamp":1775228674607,"version":"3.50.1"},"reference-count":41,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"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":[[2022,8,21]]},"DOI":"10.1109\/icpr56361.2022.9956084","type":"proceedings-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T19:34:13Z","timestamp":1669750453000},"page":"3376-3382","source":"Crossref","is-referenced-by-count":13,"title":["Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients"],"prefix":"10.1109","author":[{"given":"Riccardo","family":"Zaccone","sequence":"first","affiliation":[{"name":"Politecnico di Torino,Turin,Italy"}]},{"given":"Andrea","family":"Rizzardi","sequence":"additional","affiliation":[{"name":"Politecnico di Torino,Turin,Italy"}]},{"given":"Debora","family":"Caldarola","sequence":"additional","affiliation":[{"name":"Politecnico di Torino,Turin,Italy"}]},{"given":"Marco","family":"Ciccone","sequence":"additional","affiliation":[{"name":"Politecnico di Torino,Turin,Italy"}]},{"given":"Barbara","family":"Caputo","sequence":"additional","affiliation":[{"name":"Politecnico di Torino,Turin,Italy"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Multi-center federated learning","author":"xie","year":"2021"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71246-8_49"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1348\/000711005X48266"},{"key":"ref31","article-title":"Federated multi-task learning","author":"smith","year":"2017"},{"key":"ref30","article-title":"Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints","author":"sattler","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71050-9"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20050-2_41"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0033314"},{"key":"ref34","article-title":"A distillation-based approach integrating continual learning and federated learning for pervasive services","author":"usmanova","year":"2021"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s41666-020-00082-4"},{"key":"ref11","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"hsu","year":"2019","journal-title":"NeurIPS workshop"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_5"},{"key":"ref13","article-title":"Decentralized federated learning: A segmented gossip approach","author":"hu","year":"2019"},{"key":"ref14","article-title":"Advances and open problems in federated learning","author":"kairouz","year":"2019"},{"key":"ref15","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","author":"karimireddy","year":"2020","journal-title":"International Conference on Machine Learning"},{"key":"ref16","article-title":"First analysis of local gd on heterogeneous data","author":"khaled","year":"2019"},{"key":"ref17","article-title":"Fedfmc: Sequential efficient federated learning on non-iid data","author":"kopparapu","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"ref19","article-title":"Fedmd: Heterogenous federated learning via model distillation","author":"li","year":"2019"},{"key":"ref28","article-title":"Adaptive federated optimization","author":"reddi","year":"2021","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref4","first-page":"1","article-title":"Federated learning with hierarchical clustering of local updates to improve training on noniid data","author":"briggs","year":"2020","journal-title":"2020 International Joint Conference on Neural Networks (IJCNN)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1037\/met0000301"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1126\/science.153.3731.34"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask learning","volume":"28","author":"caruana","year":"1997","journal-title":"Machine Learning"},{"key":"ref29","article-title":"Braintorrent: A peer-to-peer environment for decentralized federated learning","author":"roy","year":"2019"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00309"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.4169\/000298910x523344"},{"key":"ref7","article-title":"Personalized federated learning: A meta-learning approach","author":"fallah","year":"2020"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00653"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"Liii on lines and planes of closest fit to systems of points in space","volume":"2","author":"karl","year":"1901","journal-title":"The London Edinburgh and Dublin Philosophical Magazine and Journal of Science"},{"key":"ref1","article-title":"Federated learning based on dynamic regularization","author":"emre acar","year":"2021","journal-title":"International Conference on Learning Representations"},{"key":"ref20","article-title":"Federated learning on non-iid data silos: An experimental study","author":"li","year":"2021","journal-title":"CoRR"},{"key":"ref22","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"li","year":"2020","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref24","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":"ref41","article-title":"Federated learning with non-iid data","author":"zhao","year":"2018"},{"key":"ref23","article-title":"On the convergence of fedavg on non-iid data","author":"li","year":"2019"},{"key":"ref26","first-page":"7705","article-title":"Comparative analysis of anti-clusters formed using various distance metrics and k-medoids algorithm","volume":"29","author":"reddy","year":"2020","journal-title":"International Journal of Advanced Science and Technology"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00263"}],"event":{"name":"2022 26th International Conference on Pattern Recognition (ICPR)","location":"Montreal, QC, Canada","start":{"date-parts":[[2022,8,21]]},"end":{"date-parts":[[2022,8,25]]}},"container-title":["2022 26th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9956007\/9955631\/09956084.pdf?arnumber=9956084","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T20:07:20Z","timestamp":1671480440000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9956084\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,21]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/icpr56361.2022.9956084","relation":{},"subject":[],"published":{"date-parts":[[2022,8,21]]}}}