{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:52:21Z","timestamp":1774896741156,"version":"3.50.1"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"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":[[2024,12,15]]},"DOI":"10.1109\/bigdata62323.2024.10825355","type":"proceedings-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T18:31:23Z","timestamp":1737052283000},"page":"7790-7799","source":"Crossref","is-referenced-by-count":3,"title":["Boosting Federated Learning with Diffusion Models for Non-IID and Imbalanced Data"],"prefix":"10.1109","author":[{"given":"Maximilian Andreas","family":"Hoefler","sequence":"first","affiliation":[{"name":"Fraunhofer HHI"}]},{"given":"Tatsiana","family":"Mazouka","sequence":"additional","affiliation":[{"name":"Fraunhofer HHI"}]},{"given":"Karsten","family":"Mueller","sequence":"additional","affiliation":[{"name":"Fraunhofer HHI"}]},{"given":"Wojciech","family":"Samek","sequence":"additional","affiliation":[{"name":"Technical University,Fraunhofer HHI,Berlin"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1"},{"key":"ref4","article-title":"Federated learning for mobile keyboard prediction","volume-title":"Proceedings of the 2nd Conference on Systems and Machine Learning (SysML)","author":"Hard"},{"key":"ref5","first-page":"728","article-title":"Fmvss: Federated multi-variate statistical synthesis for financial data","volume-title":"2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS)","author":"Yang"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2942190"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref9","first-page":"30 882","article-title":"Tct: Convexifying federated learning using bootstrapped neural tangent kernels","volume-title":"Advances in Neural Information Processing Systems","volume":"35","author":"Yu","year":"2022"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00386"},{"key":"ref11","article-title":"Federated learning via synthetic data","volume-title":"CoRR","author":"Goetz","year":"2020"},{"key":"ref12","article-title":"Diffusion models beat gans on image synthesis","author":"Dhariwal","year":"2021"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.02155"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_5"},{"key":"ref15","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proceedings of the 37th International Conference on Machine Learning","volume":"119","author":"Karimireddy"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref17","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"Wang"},{"key":"ref18","article-title":"Fed{bn}: Federated learning on non-{iid} features via local batch normalization","volume-title":"International Conference on Learning Representations","author":"Li"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00531"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-28996-5_2"},{"key":"ref21","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proceedings of the 32nd International Conference on Machine Learning","volume":"37","author":"Sohl-Dickstein"},{"key":"ref22","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Ho","year":"2020"},{"key":"ref23","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proceedings of the 38th International Conference on Machine Learning","volume":"139","author":"Nichol"},{"key":"ref24","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"International Conference on Learning Representations","author":"Song"},{"key":"ref25","article-title":"Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity","author":"Li","year":"2023"},{"issue":"15","key":"ref26","first-page":"16 325","article-title":"Exploring one-shot semi-supervised federated learning with pre-trained diffusion models","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"38","author":"Yang"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-8546-3_18"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3589335.3651935"},{"key":"ref29","article-title":"No fear of heterogeneity: classifer calibration for federated learning with non-iid data","volume-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems","author":"Luo"},{"key":"ref30","article-title":"Insulator defect detection","author":"Kulkarni","year":"2021"},{"key":"ref31","article-title":"Xai-guided insulator anomaly detection for imbalanced datasets","author":"Hoefler","year":"2024"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI48211.2021.9434062"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01721-8"},{"key":"ref34","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"ref35","doi-asserted-by":"crossref","DOI":"10.1109\/SaTML59370.2024.00010","article-title":"Shake to leak: Fine-tuning diffusion models can amplify the generative privacy risk","author":"Li","year":"2024"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"}],"event":{"name":"2024 IEEE International Conference on Big Data (BigData)","location":"Washington, DC, USA","start":{"date-parts":[[2024,12,15]]},"end":{"date-parts":[[2024,12,18]]}},"container-title":["2024 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10824975\/10824942\/10825355.pdf?arnumber=10825355","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T08:14:46Z","timestamp":1737101686000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10825355\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,15]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/bigdata62323.2024.10825355","relation":{},"subject":[],"published":{"date-parts":[[2024,12,15]]}}}