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A Novel Federated Learning Scheme for Generative Adversarial Networks

Zhang, Jiaxin; Zhao, Liang; Yu, Keping; Min, Geyong; Al-Dubai, Ahmed; Zomya, Albert

Authors

Jiaxin Zhang

Liang Zhao

Keping Yu

Geyong Min

Albert Zomya



Abstract

Generative adversarial networks (GANs) have been advancing and gaining tremendous interests from both academia and industry. With the development of wireless technologies, a huge amount of data generated at the network edge provides an unprecedented opportunity to develop GANs applications. However, due to the constraints such as bandwidth, privacy, and legal issues, it is inappropriate to collect and send all data to the cloud or servers for analysis, training, and mining. Thus, deploying and training GANs at the edge becomes a promising alternative solution. The instability of GANs introduced by non-independent and identical data (Non-IID) poses significant challenges to training GANs. To address these challenges, this paper presents a novel federated learning framework for GANs, namely, Collaborated gAme Parallel Learning (CAP). CAP supports parallel training of data and models for GANs, breaking the isolated training among generators that exists in the previous distributed algorithms, and achieving collaborative learning among cloud, edge servers, and devices. Then, to further enhance the ability of CAP-GAN for addressing Non-IID issues, we propose a Mix-Generator module (Mix-G) which divides a generator into the sharing layer and personalizing layer. The Mix-G module extracts the generic and personalization features and improves the performance of CAP GAN on extremely personalizing datasets. Experimental results and analysis substantiate the usefulness and superiority of our proposed CAP-GAN scheme which can achieve better results in the Non-IID scenarios compared with the state-of-the-art algorithms.

Journal Article Type Article
Acceptance Date May 15, 2023
Online Publication Date May 22, 2023
Publication Date 2024-05
Deposit Date May 15, 2023
Publicly Available Date May 22, 2023
Print ISSN 1536-1233
Electronic ISSN 1558-0660
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 23
Issue 5
Pages 3633-3649
DOI https://doi.org/10.1109/tmc.2023.3278668
Keywords Generative adversarial networks, distributed learning and deployment, collaborative learning, non-independent and identical data
Public URL http://researchrepository.napier.ac.uk/Output/3102676

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