{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T05:33:49Z","timestamp":1770356029481,"version":"3.49.0"},"reference-count":19,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,10,8]],"date-time":"2023-10-08T00:00:00Z","timestamp":1696723200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,8]],"date-time":"2023-10-08T00:00:00Z","timestamp":1696723200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015803","name":"Tencent","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015803","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,8]]},"DOI":"10.1109\/icip49359.2023.10222143","type":"proceedings-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T17:58:31Z","timestamp":1694455111000},"page":"2180-2184","source":"Crossref","is-referenced-by-count":3,"title":["FEDMBP: Multi-Branch Prototype Federated Learning on Heterogeneous Data"],"prefix":"10.1109","author":[{"given":"Tianrun","family":"Gao","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications,State Key Lab of Networking and Switching Technology,Beijing,China"}]},{"given":"Xiaohong","family":"Liu","sequence":"additional","affiliation":[{"name":"UCL Cancer Institute, University College London,London,United Kingdom"}]},{"given":"Yuning","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications,State Key Lab of Networking and Switching Technology,Beijing,China"}]},{"given":"Guangyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications,State Key Lab of Networking and Switching Technology,Beijing,China"}]}],"member":"263","reference":[{"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":"ref2","article-title":"Federated optimization: Distributed optimization beyond the datacenter","author":"Kone\u010dn\u1ef3","year":"2015"},{"key":"ref3","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Kone\u010dn\u1ef3","year":"2016"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.09.027"},{"key":"ref5","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine learning and systems","volume":"2","author":"Li"},{"key":"ref6","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":"ref7","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","volume-title":"International Conference on Machine Learning","author":"Karimireddy"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref9","first-page":"11058","article-title":"Multi-level branched regularization for federated learning","volume-title":"International Conference on Machine Learning","author":"Kim"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"ref11","article-title":"Prototype guided federated learning of visual feature representations","author":"Michieli","year":"2021"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16437-8_2"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref14","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161"},{"issue":"11","key":"ref16","article-title":"Visualizing data using t-sne","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of machine learning research"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref18","article-title":"Federated learning with personalization layers","author":"Arivazhagan","year":"2019"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-022-01046-x"}],"event":{"name":"2023 IEEE International Conference on Image Processing (ICIP)","location":"Kuala Lumpur, Malaysia","start":{"date-parts":[[2023,10,8]]},"end":{"date-parts":[[2023,10,11]]}},"container-title":["2023 IEEE International Conference on Image Processing (ICIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10221937\/10221892\/10222143.pdf?arnumber=10222143","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T20:17:50Z","timestamp":1709324270000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10222143\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,8]]},"references-count":19,"URL":"https:\/\/doi.org\/10.1109\/icip49359.2023.10222143","relation":{},"subject":[],"published":{"date-parts":[[2023,10,8]]}}}