{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T13:28:46Z","timestamp":1780752526252,"version":"3.54.1"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"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":[[2023,5,28]]},"DOI":"10.1109\/icc45041.2023.10279714","type":"proceedings-article","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T13:54:10Z","timestamp":1698069250000},"page":"1982-1987","source":"Crossref","is-referenced-by-count":20,"title":["Personalized Decentralized Federated Learning with Knowledge Distillation"],"prefix":"10.1109","author":[{"given":"Eunjeong","family":"Jeong","sequence":"first","affiliation":[{"name":"EURECOM,Communication Systems Department,Sophia Antipolis,France,06410"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marios","family":"Kountouris","sequence":"additional","affiliation":[{"name":"EURECOM,Communication Systems Department,Sophia Antipolis,France,06410"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","first-page":"509","article-title":"Decentralized col-laborative learning of personalized models over networks","author":"vanhaesebrouck","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref12","article-title":"Unifying distillation with personal-ization in federated learning","author":"divi","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref15","article-title":"A decentralized collaborative learning framework across heterogeneous devices for personalized predictive analytics","author":"ye","year":"2022","journal-title":"ArXiv Preprint"},{"key":"ref14","first-page":"864","article-title":"Fully decentralized joint learning of personalized models and collaboration graphs","author":"zantedeschi","year":"2020","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref11","article-title":"Improving feder-ated learning personalization via model agnostic meta learning","author":"jiang","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref10","article-title":"Three approaches for personalization with applications to federated learning","author":"mansour","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"ref1","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?ny","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref17","article-title":"Decentralized personalized federated min-max problems","author":"borodich","year":"2021","journal-title":"Workshop on New Frontiers in Federated Learning (in Conjunction with NeurIPS 2021)"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00954"},{"key":"ref19","first-page":"473","article-title":"Personalized and private peer-to-peer machine learning","author":"bellet","year":"2018","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejco.2022.100041"},{"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":"ref23","article-title":"Large scale distributed neural network training through online distillation","author":"anil","year":"2018","journal-title":"International Conference on Learning Representations"},{"key":"ref26","article-title":"Federated learning with matched averaging","author":"wang","year":"2020","journal-title":"International Conference on Learning Representations"},{"key":"ref25","article-title":"On first-order meta-learning algorithms","author":"nichol","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref20","first-page":"15434","article-title":"Federated multitask learning under a mixture of distributions","volume":"34","author":"marfoq","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref22","article-title":"Distilling the knowledge in a neural network","volume":"2","author":"hinton","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref21","article-title":"Communication-efficient on-device machine learning: Federated dis-tillation and augmentation under non-iid private data","author":"jeong","year":"2018","journal-title":"Workshop on Machine Learning on the Phone and other Consumer Devices (in Conjundtion with NeurIPS 2018)"},{"key":"ref28","article-title":"Re-fined convergence and topology learning for decentralized optimization with heterogeneous data","author":"le bars","year":"2022","journal-title":"Workshop on Federated Learning Recent Advances and New Challenges (in Conjunction with NeurIPS 2022)"},{"key":"ref27","article-title":"On the convergence of fedavg on non-iid data","author":"li","year":"2020","journal-title":"International Conference on Learning Representations"},{"key":"ref29","article-title":"Federated multitask learning","volume":"30","author":"smith","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref8","article-title":"Personalized federated learning: A meta-learning approach","author":"fallah","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref7","article-title":"Personalized federated learning: An attentive collaboration approach","volume":"abs 2007 3797","author":"huang","year":"2020","journal-title":"CoRR"},{"key":"ref9","article-title":"Motley: Benchmarking heterogeneity and personalization in federated learning","author":"wu","year":"2022","journal-title":"Workshop on Federated Learning Recent Advances and New Challenges (in Conjunction with NeurIPS 2022)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref3","first-page":"16937","article-title":"Inverting gradients - how easy is it to break privacy in federated learning?","volume":"33","author":"geiping","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref6","first-page":"15070","article-title":"Personalized federated learning through local memorization","author":"marfoq","year":"2022","journal-title":"International Conference on Machine Learning"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900461"}],"event":{"name":"ICC 2023 - IEEE International Conference on Communications","location":"Rome, Italy","start":{"date-parts":[[2023,5,28]]},"end":{"date-parts":[[2023,6,1]]}},"container-title":["ICC 2023 - IEEE International Conference on Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10278505\/10278554\/10279714.pdf?arnumber=10279714","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T14:01:44Z","timestamp":1699884104000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10279714\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,28]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/icc45041.2023.10279714","relation":{},"subject":[],"published":{"date-parts":[[2023,5,28]]}}}