{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T15:20:48Z","timestamp":1793114448687,"version":"4.5.0"},"reference-count":70,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"German Federal Ministry of Education and Research (BMBF) through the Berlin Institute for the Foundations of Learning and Data","doi-asserted-by":"publisher","award":["01IS18025A"],"award-info":[{"award-number":["01IS18025A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"German Federal Ministry of Education and Research (BMBF) through the Berlin Institute for the Foundations of Learning and Data","doi-asserted-by":"publisher","award":["01IS18037I"],"award-info":[{"award-number":["01IS18037I"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EU\u2019s Horizon 2020 Project COPA EUROPE","award":["957059"],"award-info":[{"award-number":["957059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1109\/tnnls.2021.3129371","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T15:31:14Z","timestamp":1638372674000},"page":"5531-5543","source":"Crossref","is-referenced-by-count":102,"title":["FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9425-2238","authenticated-orcid":false,"given":"Felix","family":"Sattler","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8209-4079","authenticated-orcid":false,"given":"Tim","family":"Korjakow","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2657-2811","authenticated-orcid":false,"given":"Roman","family":"Rischke","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6283-3265","authenticated-orcid":false,"given":"Wojciech","family":"Samek","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist. (AISTATS)","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3124599"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10922-021-09609-5"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2021.102748"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-69250-1"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.007"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref8","article-title":"Pointer sentinel mixture models","author":"Merity","year":"2016","journal-title":"arXiv:1609.07843"},{"key":"ref9","first-page":"4615","article-title":"Agnostic federated learning","volume-title":"Proc. 36th Int. Conf. Mach. Learn. (ICML)","author":"Mohri"},{"key":"ref10","article-title":"Adaptive federated optimization","author":"Reddi","year":"2020","journal-title":"arXiv:2003.00295"},{"key":"ref11","first-page":"1","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst. (MLSys)","author":"Li"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3070013"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3081748"},{"key":"ref15","first-page":"1","article-title":"Ensemble distillation for robust model fusion in federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"33","author":"Lin"},{"key":"ref16","article-title":"Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data","author":"Jeong","year":"2018","journal-title":"arXiv:1811.11479"},{"key":"ref17","article-title":"FedBE: Making Bayesian model ensemble applicable to federated learning","author":"Chen","year":"2020","journal-title":"arXiv:2009.01974"},{"key":"ref18","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015","journal-title":"arXiv:1503.02531"},{"key":"ref19","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. 37th Int. Conf. Mach. Learn. (ICML)","author":"Chen"},{"key":"ref20","first-page":"9929","article-title":"Understanding contrastive representation learning through alignment and uniformity on the hypersphere","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wang"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.18653\/vl\/N19-142"},{"issue":"8","key":"ref22","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI blog"},{"key":"ref23","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018","journal-title":"arXiv:1806.00582"},{"key":"ref24","first-page":"1","article-title":"On the convergence of FedAvg on non-iid data","volume-title":"Proc. 8th Int. Conf. Learn. Represent. (ICLR)","author":"Li"},{"key":"ref25","first-page":"1041","article-title":"Domain adaptation with multiple sources","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"21","author":"Mansour"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-022-09791-5"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-021-02482-2"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"ref29","article-title":"Deep semi-supervised anomaly detection","author":"Ruff","year":"2019","journal-title":"arXiv:1906.02694"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref31","first-page":"1069","article-title":"Differentially private empirical risk minimization","volume":"12","author":"Chaudhuri","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref33","article-title":"FedMD: Heterogenous federated learning via model distillation","author":"Li","year":"2019","journal-title":"arXiv:1910.03581"},{"key":"ref34","article-title":"Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer","author":"Chang","year":"2019","journal-title":"arXiv:1912.11279"},{"key":"ref35","article-title":"FedH2L: Federated learning with model and statistical heterogeneity","author":"Li","year":"2021","journal-title":"arXiv:2101.11296"},{"key":"ref36","article-title":"Federated knowledge distillation","author":"Seo","year":"2020","journal-title":"arXiv:2011.02367"},{"key":"ref37","article-title":"One-shot federated learning","author":"Guha","year":"2019","journal-title":"arXiv:1902.11175"},{"key":"ref38","article-title":"Federated model distillation with noise-free differential privacy","author":"Sun","year":"2020","journal-title":"arXiv:2009.05537"},{"key":"ref39","article-title":"Distilled one-shot federated learning","author":"Zhou","year":"2020","journal-title":"arXiv:2009.07999"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC.2019.8904164"},{"key":"ref41","first-page":"617","article-title":"Approximating a function and its derivatives using mse-optimal linear combinations of trained feedforward neural networks","volume-title":"Proc. World Congr. Neural Netw.","volume":"1","author":"Hashem"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1142\/9789812795885_0025"},{"key":"ref43","first-page":"190","article-title":"Learning with ensembles: How overfitting can be useful","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"8","author":"Sollich"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1080\/095400996116785"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1613\/jair.614"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.1998.682375"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2200299"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-012-9338-y"},{"key":"ref49","first-page":"1401","article-title":"A brief introduction to boosting","volume-title":"Proc. 16th Int. Joint Conf. Artif. Intell. (IJCAI)","author":"Schapire"},{"key":"ref50","first-page":"4424","article-title":"Federated multi-task learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"30","author":"Smith"},{"key":"ref51","article-title":"A theoretical perspective on differentially private federated multi-task learning","author":"Wu","year":"2020","journal-title":"arXiv:2011.07179"},{"key":"ref52","article-title":"Robust federated learning in a heterogeneous environment","author":"Ghosh","year":"2019","journal-title":"arXiv:1906.06629"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref54","article-title":"Improving semi-supervised federated learning by reducing the gradient diversity of models","author":"Zhang","year":"2020","journal-title":"arXiv:2008.11364"},{"key":"ref55","article-title":"Federated semi-supervised learning with inter-client consistency & disjoint learning","author":"Jeong","year":"2020","journal-title":"arXiv:2006.12097"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1631\/fitee.2200268"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.372"},{"key":"ref61","first-page":"649","article-title":"Character-level convolutional networks for text classification","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"28","author":"Zhang"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.369"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.11"},{"key":"ref64","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Harry Hsu","year":"2019","journal-title":"arXiv:1909.06335"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/BF01589116"},{"key":"ref66","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref67","first-page":"1","article-title":"Scalable private learning with PATE","volume-title":"Proc. 6th Int. Conf. Learn. Represent. (ICLR)","author":"Papernot"},{"key":"ref68","article-title":"Data-free knowledge distillation for heterogeneous federated learning","author":"Zhu","year":"2021","journal-title":"arXiv:2105.10056"},{"key":"ref69","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bhagoji"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3077803"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10237282\/09632275.pdf?arnumber=9632275","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T14:49:48Z","timestamp":1709304588000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9632275\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":70,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2021.3129371","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9]]}}}