{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:00:37Z","timestamp":1772553637928,"version":"3.50.1"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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","award":["62102445"],"award-info":[{"award-number":["62102445"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002369"],"award-info":[{"award-number":["62002369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017684","name":"Postgraduate Scientific Research Innovation Project of Hunan Province","doi-asserted-by":"publisher","award":["CX20210033"],"award-info":[{"award-number":["CX20210033"]}],"id":[{"id":"10.13039\/501100017684","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1109\/tnnls.2024.3405190","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T17:35:07Z","timestamp":1723484107000},"page":"88-101","source":"Crossref","is-referenced-by-count":12,"title":["Improving Generalization and Personalization in Model-Heterogeneous Federated Learning"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8399-5175","authenticated-orcid":false,"given":"Xiongtao","family":"Zhang","sequence":"first","affiliation":[{"name":"Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4199-2793","authenticated-orcid":false,"given":"Ji","family":"Wang","sequence":"additional","affiliation":[{"name":"Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1867-3660","authenticated-orcid":false,"given":"Weidong","family":"Bao","sequence":"additional","affiliation":[{"name":"Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0242-3408","authenticated-orcid":false,"given":"Yaohong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1301-7840","authenticated-orcid":false,"given":"Xiaomin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Strategic Assessments and Consultation Institute, Academy of Military Science, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0458-5977","authenticated-orcid":false,"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Development Environment, School of Cyber Science and Technology, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6339-0219","authenticated-orcid":false,"given":"Xiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist. Conf.","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20643"},{"key":"ref5","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan"},{"key":"ref6","first-page":"1012","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bhagoji"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2953131"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref9","first-page":"7142","article-title":"Sagda: Achieving o(\u03f5-2) communication complexity in federated min-max learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yang"},{"key":"ref10","first-page":"29677","article-title":"Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alam"},{"key":"ref11","first-page":"4270","article-title":"Resource-adaptive federated learning with all-in-one neural composition","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Mei"},{"key":"ref12","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","volume":"119","author":"Karimireddy"},{"key":"ref13","first-page":"26311","article-title":"Federated learning with label distribution skew via logits calibration","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref14","first-page":"26293","article-title":"Personalized federated learning via variational Bayesian inference","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref15","first-page":"15070","article-title":"Personalized federated learning through local memorization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Marfoq"},{"key":"ref16","first-page":"1","article-title":"FedP3: Federated personalized and privacy-friendly network pruning under model heterogeneity","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Yi"},{"key":"ref17","article-title":"Internal cross-layer gradients for extending homogeneity to heterogeneity in federated learning","author":"Chan","year":"2023","journal-title":"arXiv:2308.11464"},{"key":"ref18","first-page":"21414","article-title":"Dense: Data-free one-shot federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref19","first-page":"1","article-title":"An agnostic approach to federated learning with class imbalance","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3192475"},{"key":"ref21","first-page":"1","article-title":"Decoupling representation and classifier for long-tailed recognition","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kang"},{"key":"ref22","first-page":"5132","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Annu. Conf. Mach. Learn. Syst.","author":"Li"},{"key":"ref23","first-page":"1","article-title":"On bridging generic and personalized federated learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref24","first-page":"1","article-title":"FedMD: Heterogenous federated learning via model distillation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref25","first-page":"10092","article-title":"Parameterized knowledge transfer for personalized federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref26","first-page":"1","article-title":"Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jeong"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"ref28","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref29","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref30","first-page":"37860","article-title":"Personalized federated learning under mixture of distributions","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref31","first-page":"1","article-title":"The best of both worlds accurate global and personalized models through federated learning with datafree hyper-knowledge distillation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref32","first-page":"15434","article-title":"Federated multi-task learning under a mixture of distributions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Marfoq"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i2.20057"},{"key":"ref34","article-title":"FedLoGe: Joint local and generic federated learning under long-tailed data","author":"Xiao","year":"2024","journal-title":"arXiv:2401.08977"},{"key":"ref35","article-title":"Demystifying local and global fairness tradeoffs in federated learning using partial information decomposition","author":"Hamman","year":"2023","journal-title":"arXiv:2307.11333"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00465"},{"key":"ref37","first-page":"1","article-title":"Towards model agnostic federated learning using knowledge distillation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Afonin"},{"key":"ref38","article-title":"Data-free knowledge distillation for deep neural networks","author":"Lopes","year":"2017","journal-title":"arXiv:1710.07535"},{"key":"ref39","article-title":"Data-free adversarial distillation","author":"Fang","year":"2019","journal-title":"arXiv:1912.11006"},{"key":"ref40","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proc. NeurIPS Conf.","volume":"33","author":"Fallah"},{"key":"ref41","first-page":"21394","article-title":"Personalized federated learning with Moreau envelopes","volume-title":"Proc. NIPS","author":"Dinh"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3241211"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3105284"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.323"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00152"},{"key":"ref46","article-title":"LEAF: A benchmark for federated settings","author":"Caldas","year":"2018","journal-title":"arXiv:1812.01097"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref48","first-page":"43158","article-title":"Leadfl: Client self-defense against model poisoning in federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3345367"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/10832116\/10633723.pdf?arnumber=10633723","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T20:03:53Z","timestamp":1736971433000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10633723\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":49,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3405190","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}