{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:18:36Z","timestamp":1769552316451,"version":"3.49.0"},"reference-count":42,"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\/ijcnn55064.2022.9892585","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T19:56:04Z","timestamp":1664567764000},"page":"01-08","source":"Crossref","is-referenced-by-count":18,"title":["FedPer++: Toward Improved Personalized Federated Learning on Heterogeneous and Imbalanced Data"],"prefix":"10.1109","author":[{"given":"Jian","family":"Xu","sequence":"first","affiliation":[{"name":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University"}]},{"given":"Yi","family":"Yan","sequence":"additional","affiliation":[{"name":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University"}]},{"given":"Shao-Lun","family":"Huang","sequence":"additional","affiliation":[{"name":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua University"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Federated multi-task learning under a mixture of distributions","author":"marfoq","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref38","first-page":"9489","article-title":"Personalized federated learning using hypernetworks","volume":"139","author":"shamsian","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning ICML 2021"},{"key":"ref33","article-title":"An efficient framework for clustered federated learning","author":"ghosh","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref32","first-page":"2089","article-title":"Exploiting shared representations for personalized federated learning","volume":"139","author":"collins","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning ICML 2021"},{"key":"ref31","article-title":"Think locally, act globally: Federated learning with local and global representations","author":"liang","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref30","article-title":"Debiasing model updates for improving personalized federated training","volume":"icml 2021","author":"acar","year":"0","journal-title":"Proceedings of the 38th International Conference on Machine Learning"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"ref36","article-title":"Personalized federated learning with first order model optimization","author":"zhang","year":"2021","journal-title":"9th International Conference on Learning Representations ICLR 2021"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref34","article-title":"Three approaches for personalization with applications to federated learning","author":"mansour","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/WorldS450073.2020.9210355"},{"key":"ref40","article-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","volume":"abs 1708 7747","author":"xiao","year":"2017","journal-title":"CoRR"},{"key":"ref11","article-title":"Towards personalized federated learning","author":"tan","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref12","first-page":"4424","article-title":"Federated multi-task learning","author":"smith","year":"2017","journal-title":"Advances in Neural Information Processing Systems 30 Annual Conference on Neural Information Processing Systems 2017"},{"key":"ref13","article-title":"Federated learning with personalization layers","author":"arivazhagan","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref14","article-title":"Federated learning based on dynamic regularization","author":"acar","year":"2021","journal-title":"9th International Conference on Learning Representations ICLR 2021"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT45174.2021.9517850"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_5"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17219"},{"key":"ref18","article-title":"Fedmix: Approximation of mixup under mean augmented federated learning","author":"yoon","year":"2021","journal-title":"9th International Conference on Learning Representations ICLR 2021"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref28","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume":"139","author":"li","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning ICML 2021"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref27","article-title":"Personalized federated learning with moreau envelopes","author":"dinh","year":"0","journal-title":"Conference on Neural Information Processing Systems"},{"key":"ref3","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"mcmahan","year":"0","journal-title":"International Conference on Artificial Intelligence and Statistics (AISTATS)"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"key":"ref29","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume":"neurips 2020","author":"fallah","year":"2020","journal-title":"Advances in Neural Information Processing Systems 33 Annual Conference on Neural Information Processing Systems 2020"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref8","first-page":"5132","article-title":"SCAFFOLD: stochastic controlled averaging for federated learning","volume":"119","author":"karimireddy","year":"2020","journal-title":"Proceedings of the 37th International Conference on Machine Learning ICML 2020"},{"key":"ref7","article-title":"Federated optimization in heterogeneous networks","volume":"mlsys 2020","author":"li","year":"2020","journal-title":"Proceedings of Machine Learning and Systems 2020"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref20","article-title":"Fedgp: Correlation-based active client selection for heterogeneous federated learning","volume":"abs 2103 13822","author":"tang","year":"2021","journal-title":"CoRR"},{"key":"ref22","article-title":"Ensemble distillation for robust model fusion in federated learning","author":"lin","year":"0","journal-title":"Advances in Neural Information Processing Systems 33 Annual Conference on Neural Information Processing Systems 2020 NeurIPS 2020"},{"key":"ref21","first-page":"3407","article-title":"Clustered sampling: Low-variance and improved representativity for clients selection in federated learning","volume":"139","author":"fraboni","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning ICML 2021"},{"key":"ref42","article-title":"Fedproto: Fed-erated prototype learning over heterogeneous devices","volume":"abs 2105 243","author":"tan","year":"2021","journal-title":"CoRR"},{"key":"ref24","article-title":"Salvaging federated learning by local adaptation","author":"yu","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref41","author":"krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images"},{"key":"ref23","first-page":"12878","article-title":"Data-free knowledge distillation for het-erogeneous federated learning","volume":"139","author":"zhu","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning ICML 2021"},{"key":"ref26","article-title":"Adaptive personalized federated learning","author":"deng","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref25","article-title":"Federated learning of a mixture of global and local models","author":"hanzely","year":"2020","journal-title":"ArXiv Preprint"}],"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\/09892585.pdf?arnumber=9892585","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:01:03Z","timestamp":1667516463000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892585\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892585","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}