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Generative adversarial networks have gained plenty of attention in the research community especially with their abilities to produce high quality synthetic data for a variety of use-cases. Yet, when combined with federated learning, those models suffer from degradation in both training time and quality of results. To address this challenge, this paper introduces a novel approach that uses hierarchical learning techniques to enable the efficient training of federated GAN models. The proposed approach introduces an innovative mechanism that dynamically clusters participant clients to edge servers as well as a novel multi-generator GAN architecture that utilizes non-identical model aggregation stages. The proposed approach has been evaluated on a number of benchmark datasets to measure its performance on higher numbers of participating clients. The results show that HFL-GAN outperforms other comparative state-of-the-art approaches in the training of GAN models in complex non-IID federated learning settings.<\/jats:p>","DOI":"10.1007\/s10489-024-05924-x","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T07:28:58Z","timestamp":1734334138000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["HFL-GAN: scalable hierarchical federated learning GAN for high quantity heterogeneous clients"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2165-1165","authenticated-orcid":false,"given":"Lewis","family":"Petch","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Moustafa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhui","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Yasser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"5924_CR1","doi-asserted-by":"publisher","unstructured":"Jiang L, Dai B, Wu W, Loy CC Deceive D (2021) Adaptive Pseudo Augmentation for GAN Training with Limited Data. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2111.06849","DOI":"10.48550\/arXiv.2111.06849"},{"key":"5924_CR2","doi-asserted-by":"publisher","unstructured":"Liu B, Ding M, Shaham S, Rahayu W, Farokhi F, Lin Z (2021) When machine learning meets privacy: A survey and outlook 54(2):31\u201313136 https:\/\/doi.org\/10.1145\/3436755","DOI":"10.1145\/3436755"},{"key":"5924_CR3","doi-asserted-by":"publisher","unstructured":"McMahan HB, Moore E, Ramage D, Hampson S, Arcas BAy (2023) Communication-efficient learning of deep networks from decentralized data. arXiv. https:\/\/doi.org\/10.48550\/arXiv.1602.05629","DOI":"10.48550\/arXiv.1602.05629"},{"key":"5924_CR4","doi-asserted-by":"publisher","unstructured":"Saxena D, Cao J (2021) Generative adversarial networks (GANs): Challenges, solutions, and future directions 54(3):63\u201316342 https:\/\/doi.org\/10.1145\/3446374","DOI":"10.1145\/3446374"},{"key":"5924_CR5","doi-asserted-by":"publisher","unstructured":"Fui-Hoon Nah F, Zheng R, Cai J, Siau K, Chen L (2023) Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration 25(3):277\u2013304 https:\/\/doi.org\/10.1080\/15228053.2023.2233814","DOI":"10.1080\/15228053.2023.2233814"},{"key":"5924_CR6","doi-asserted-by":"publisher","unstructured":"Wang K, Gou C, Duan Y, Lin Y, Zheng X, Wang F-Y (2017) Generative adversarial networks: introduction and outlook 4(4):588\u2013598 https:\/\/doi.org\/10.1109\/JAS.2017.7510583. 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