{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:12:09Z","timestamp":1777129929356,"version":"3.51.4"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"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":[[2022,7,18]]},"DOI":"10.1109\/ijcnn55064.2022.9892653","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T19:56:04Z","timestamp":1664567764000},"page":"1-8","source":"Crossref","is-referenced-by-count":8,"title":["Federated learning with incremental clustering for heterogeneous data"],"prefix":"10.1109","author":[{"given":"Fabiola","family":"Espinoza Castellon","sequence":"first","affiliation":[{"name":"Institut LIST, CEA, Universit&#x00E9; Paris-Saclay,Palaiseau,France,F-91120"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aurelien","family":"Mayoue","sequence":"additional","affiliation":[{"name":"Institut LIST, CEA, Universit&#x00E9; Paris-Saclay,Palaiseau,France,F-91120"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacques-Henri","family":"Sublemontier","sequence":"additional","affiliation":[{"name":"Institut LIST, CEA, Universit&#x00E9; Paris-Saclay,Palaiseau,France,F-91120"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cedric","family":"Gouy-Pailler","sequence":"additional","affiliation":[{"name":"Institut LIST, CEA, Universit&#x00E9; Paris-Saclay,Palaiseau,France,F-91120"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Salvaging federated learning by local adaptation","author":"yu","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref11","article-title":"Improving federated learning personalization via model agnostic meta learning","author":"jiang","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref12","article-title":"Federated multi-task learning","volume":"30","author":"smith","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref13","article-title":"Federated multi-task learning under a mixture of distributions","author":"marfoq","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref14","first-page":"19 586","article-title":"An efficient frame-work for clustered federated learning","volume":"33","author":"ghosh","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"key":"ref17","article-title":"Federated learning for mobile keyboard prediction","author":"hard","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref18","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","author":"yin","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref19","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume":"30","author":"blanchard","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref4","article-title":"Advances and open problems in federated learning","author":"kairouz","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref3","first-page":"4387","article-title":"The non-iid data quagmire of decentralized machine learning","author":"hsieh","year":"2020","journal-title":"International Conference on Machine Learning"},{"key":"ref6","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume":"119","author":"karimireddy","year":"0","journal-title":"Proc of the International Conference on Machine Learning (ICML)"},{"key":"ref5","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"li","year":"0","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"ref8","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in byzantium","author":"guerraoui","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref7","article-title":"Proxskip: Yes! local gradient steps provably lead to communication acceleration! finally!","author":"mishchenko","year":"2022","journal-title":"ArXiv Preprint"},{"key":"ref2","article-title":"Federated learning with non-iid data","author":"zhao","year":"2018","journal-title":"ArXiv Preprint"},{"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":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988604"},{"key":"ref20","article-title":"Challenges and approaches for mitigating byzantine attacks in federated learning","author":"hu","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"ref23","article-title":"Learning differentially private recurrent language models","author":"mcmahan","year":"0","journal-title":"International Conference on Learning Representations"}],"event":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","location":"Padua, Italy","start":{"date-parts":[[2022,7,18]]},"end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9891857\/9889787\/09892653.pdf?arnumber=9892653","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T22:56:52Z","timestamp":1667516212000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892653\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892653","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}