{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:20:00Z","timestamp":1778048400304,"version":"3.51.4"},"reference-count":67,"publisher":"IEEE","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"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":[[2026,3,6]]},"DOI":"10.1109\/wacv61042.2026.00311","type":"proceedings-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T19:59:32Z","timestamp":1778011172000},"page":"3182-3192","source":"Crossref","is-referenced-by-count":0,"title":["Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Models"],"prefix":"10.1109","author":[{"given":"Junyi","family":"Zhu","sequence":"first","affiliation":[{"name":"Samsung R&amp;D Institute UK (SRUK)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruicong","family":"Yao","sequence":"additional","affiliation":[{"name":"KU Leuven,Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taha","family":"Ceritli","sequence":"additional","affiliation":[{"name":"Samsung R&amp;D Institute UK (SRUK)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Savas","family":"Ozkan","sequence":"additional","affiliation":[{"name":"Samsung R&amp;D Institute UK (SRUK)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew B.","family":"Blaschko","sequence":"additional","affiliation":[{"name":"KU Leuven,Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eunchung","family":"Noh","sequence":"additional","affiliation":[{"name":"Samsung Electronics Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeongwon","family":"Min","sequence":"additional","affiliation":[{"name":"Samsung Electronics Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cho Jung","family":"Min","sequence":"additional","affiliation":[{"name":"Samsung Electronics Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mete","family":"Ozay","sequence":"additional","affiliation":[{"name":"Samsung R&amp;D Institute UK (SRUK)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-024-00975-8"},{"key":"ref2","article-title":"Towards federated learning at scale: System design","volume-title":"Proceedings of the 2nd SysML Conference","author":"Bonawitz"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1137\/16m1080173"},{"key":"ref4","article-title":"Language models are few-shot learners","author":"Brown","year":"2020"},{"key":"ref5","article-title":"Leaf: A benchmark for federated settings","author":"Caldas","year":"2018"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref7","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume":"33","author":"Caron","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3369583.3392686"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3476211"},{"key":"ref10","article-title":"FedBE: Making bayesian model ensemble applicable to federated learning","volume-title":"International Conference on Learning Representations","author":"Chen"},{"key":"ref11","article-title":"Optimal client sampling for federated learning","author":"Chen","year":"2022","journal-title":"Transactions on Machine Learning Research"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref14","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","volume-title":"International Conference on Learning Representations","author":"Dosovitskiy"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO54536.2021.9616120"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2023.3310529"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00987"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2022.3192506"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2854157"},{"key":"ref21","article-title":"LoRA: Low-rank adaptation of large language models","volume-title":"International Conference on Learning Representations","author":"Hu"},{"key":"ref22","article-title":"Editing models with task arithmetic","volume-title":"The Eleventh International Conference on Learning Representations","author":"Ilharco"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"ref24","article-title":"Dataless knowledge fusion by merging weights of language models","author":"Jin","year":"2022"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref26","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Karimireddy"},{"key":"ref27","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref29","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3453142.3491287"},{"key":"ref31","first-page":"1711","article-title":"Queuing dynamics of asynchronous Federated Learning","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Leconte"},{"key":"ref32","article-title":"Fedmd: Heterogenous federated learning via model distillation","author":"Li","year":"2019"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref34","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine Learning and Systems","volume":"2","author":"Li"},{"key":"ref35","article-title":"On the convergence of fedavg on non-iid data","volume-title":"International Conference on Learning Representations","author":"Li"},{"key":"ref36","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume":"33","author":"Lin","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.334"},{"key":"ref38","article-title":"Linear combination of saved checkpoints makes consistency and diffusion models better","volume-title":"The Thirteenth International Conference on Learning Representations","author":"Liu"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29297"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref41","first-page":"1273","article-title":"Communication-Efficient learning of deep networks from decentralized data","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","author":"McMahan"},{"key":"ref42","first-page":"3581","article-title":"Federated learning with buffered asynchronous aggregation","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Nguyen"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3253853"},{"key":"ref44","article-title":"Personalized Federated Learning with Exact Stochastic Gradient Descent","author":"Nikoloutsopoulos","year":"2022"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.2858"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/462"},{"key":"ref47","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"International Conference on Machine Learning","author":"Radford"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1833"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3221401"},{"key":"ref51","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","author":"Dinh","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref52","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","author":"Wang","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref53","article-title":"Tackling the data heterogeneity in asynchronous federated learning with cached update calibration","volume-title":"International Conference on Learning Representations","author":"Wang"},{"key":"ref54","article-title":"FADAS: Towards federated adaptive asynchronous optimization","volume-title":"Forty-first International Conference on Machine Learning","author":"Wang"},{"key":"ref55","first-page":"23965","article-title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Wortsman"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29763-x"},{"key":"ref57","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref58","article-title":"Asynchronous federated optimization","volume-title":"OPT2020: 12th Annual Workshop on Optimization for Machine Learning","author":"Xie"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0310"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2639"},{"key":"ref61","article-title":"Language models are super mario: Absorbing abilities from homologous models as a free lunch","author":"Yu","year":"2023"},{"key":"ref62","article-title":"Optimizing server-side aggregation for robust federated learning via subspace training","author":"Yueqi"},{"key":"ref63","first-page":"7252","article-title":"Bayesian nonparametric federated learning of neural networks","volume-title":"International Conference on Machine Learning","author":"Yurochkin"},{"key":"ref64","first-page":"10092","article-title":"Parameterized knowledge transfer for personalized federated learning","volume":"34","author":"Zhang","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00535"},{"key":"ref66","first-page":"26293","article-title":"Personalized federated learning via variational Bayesian inference","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Zhang"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02351"}],"event":{"name":"2026 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","location":"Tucson, AZ, USA","start":{"date-parts":[[2026,3,6]]},"end":{"date-parts":[[2026,3,10]]}},"container-title":["2026 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11491838\/11491925\/11492669.pdf?arnumber=11492669","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T05:59:20Z","timestamp":1778047160000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11492669\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,6]]},"references-count":67,"URL":"https:\/\/doi.org\/10.1109\/wacv61042.2026.00311","relation":{},"subject":[],"published":{"date-parts":[[2026,3,6]]}}}