Data augmentation scheme based on conditional generative adversarial network in federated learning

doi:10.11772/j.issn.1001-9081.2024121817

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (1): 21-32.DOI: 10.11772/j.issn.1001-9081.2024121817

• Artificial intelligence • Previous Articles Next Articles

Data augmentation scheme based on conditional generative adversarial network in federated learning

Yinlong JIAN¹^,²^,³, Xuebin CHEN¹^,²^,³(), Zhongrui JING¹^,²^,³, Qi ZHONG¹^,²^,³, Zhenbo ZHANG¹^,²^,³

^1.College of Science，North China University of Science and Technology，Tangshan Hebei 063210，China
^2.Hebei Provincial Key Laboratory of Data Science and Application （North China University of Science and Technology），Tangshan Hebei 063210，China
^3.Tangshan Key Laboratory of Data Science （North China University of Science and Technology），Tangshan Hebei 063210，China

Received:2024-12-27 Revised:2025-03-04 Accepted:2025-03-10 Online:2026-01-10 Published:2026-01-10
Contact: Xuebin CHEN
About author:JIAN Yinlong， born in 2001， M. S. candidate. His research interests include data security， privacy protection.
JING Zhongrui， born in 2000， M. S. candidate. His research interests include data security， privacy protection.
ZHONG Qi， born in 1999， M. S. candidate. Her research interests include data security， privacy protection.
ZHANG Zhenbo， born in 1999， M. S. candidate. His research interests include data security， privacy protection.
Supported by:
National Natural Science Foundation of China(U20A20179)

联邦学习中基于条件生成对抗网络的数据增强方案

菅银龙¹^,²^,³, 陈学斌¹^,²^,³(), 景忠瑞¹^,²^,³, 钟琪¹^,²^,³, 张镇博¹^,²^,³

^1.华北理工大学理学院，河北唐山 063210
^2.河北省数据科学与应用重点实验室（华北理工大学），河北唐山 063210
^3.唐山市数据科学重点实验室（华北理工大学），河北唐山 063210

通讯作者: 陈学斌
作者简介:菅银龙（2001—），男，河南商丘人，硕士研究生，CCF会员，主要研究方向：数据安全、隐私保护
景忠瑞（2000—），男，山西临汾人，硕士研究生，CCF会员，主要研究方向：数据安全、隐私保护
钟琪（1999—），女，河北张家口人，硕士研究生，CCF会员，主要研究方向：数据安全、隐私保护
张镇博（1999—），男，山东济南人，硕士研究生，CCF会员，主要研究方向：数据安全、隐私保护。
基金资助:
国家自然科学基金资助项目(U20A20179)

Abstract

Abstract:

To address the challenges of slow convergence and low model accuracy in non-independent and identically distributed （Non-IID） scenarios， this paper proposed a Data Augmentation scheme based on conditional Generative Adversarial Network in Federated learning （FDA-GAN）. First， a conditional generator for class selection was designed， adding an independent network module to each class and using the label as conditional information to more accurately extract specific features for each class. Second， a client selection strategy covering all classes was proposed. Based on the comprehensive reward of the clients， a client set containing as many classes as possible was selected for training， ensuring that the Generative Adversarial Network （GAN） could learn the complete class distribution. Finally， generated samples were used to augment the local datasets of the clients， optimizing the feature composition of the local data and reducing bias between clients. Experimental results show that under DIRichlet distributed （DIR） data partitioning， compared to CAP-GAN （Collaborated gAme Parallel learning based on GAN）， FDA-GAN improves the MNIST Score （MNIST inception Score） and Mode Score by 2.67 and 1.08 respectively， and reduces the FID （Fréchet Inception Distance） and MMD （Maximum Mean Discrepancy） scores by 55.12 and 2.56 respectively； in different Non-IID scenarios， the FedAvg （Federated Averaging） and FedProx （Federated Proximal） algorithms， when combined with FDA-GAN， converge within 50 communication rounds， with accuracy improvements of at least 30.36 percentage points. This demonstrates that FDA-GAN can improve the quality and diversity of generated samples， and when combined with baseline algorithms， it can significantly improve the accuracy and convergence speed of the federated model.

Key words: Generative Adversarial Network (GAN), federated learning, Non-Independent and Identically Distributed (Non-IID), client selection, data augmentation

摘要：

针对非独立同分布（Non-IID）场景下，联邦学习系统面临收敛缓慢和模型准确率降低等挑战，提出联邦学习中基于条件生成对抗网络的数据增强方案（FDA-GAN）。首先，设计一种类别选择的条件生成器为每个类别添加独立的网络模块，并将标签作为条件信息，以更精确地提取各类别的特定特征；其次，提出一种覆盖类别的客户端选择策略来基于客户端的综合奖励，选择包含尽可能多类别的客户端集合参与训练，确保生成对抗网络（GAN）能学习到完整的类别分布；最后，利用生成样本扩充客户端的本地数据集，以优化本地数据的特征构成，减小客户端之间的偏差。实验结果表明，FDA-GAN在狄利克雷数据划分下，相较于CAP-GAN （Collaborated gAme Parallel learning based on GAN）的MNIST Score （MNIST inception Score）和Mode Score指标上分别提升了2.67和1.08，在FID （Fréchet Inception Distance）和MMD （Maximum Mean Discrepancy）指标上分别降低了55.12和2.56；在不同的Non-IID场景下， FedAvg （Federated Averaging）和FedProx （Federated Proximal）算法在结合FDA-GAN后，在50轮通信轮次内达到收敛，并且准确率提升了至少30.36个百分点。可见， FDA-GAN可以提高生成样本的质量与多样性，而且与基线算法结合后可以大幅提高联邦模型的准确率和收敛速度。

关键词: 生成对抗网络, 联邦学习, 非独立同分布, 客户端选择, 数据增强

CLC Number:

TP309.2

Yinlong JIAN, Xuebin CHEN, Zhongrui JING, Qi ZHONG, Zhenbo ZHANG. Data augmentation scheme based on conditional generative adversarial network in federated learning[J]. Journal of Computer Applications, 2026, 46(1): 21-32.

菅银龙, 陈学斌, 景忠瑞, 钟琪, 张镇博. 联邦学习中基于条件生成对抗网络的数据增强方案[J]. 《计算机应用》唯一官方网站, 2026, 46(1): 21-32.

Figures/Tables 14

Fig. 1 Overall framework of FDA-GAN

Fig. 2 Structure of conditional generator for class selection

Tab. 1 Experimental parameter setting

训练阶段	参数	值
GAN模型训练	GAN训练轮数	100
	训练批次大小	64
	生成器输入噪声维度	100
	优化算法	Adam
	优化器的学习率	0.000 2
	优化器的超参数	0.5，0.999
	客户端数	20
	客户端参与比例	0.5
联邦模型训练	全局训练轮数	200
	本地训练轮数	1
	客户端数	20
	客户端参与比例	0.5
	训练批次大小	10
	优化算法	SGD
	优化器的学习率	0.01

Fig. 3 Generation results of different schemes in three scenarios

Tab. 2 Comparison results of FID and MNIST Score metrics on MNIST dataset

方案	IID		DIR		Fully Non-IID
方案	$V F I D$	$V M N I S T S c o r e$	$V F I D$	$V M N I S T S c o r e$	$V F I D$	$V M N I S T S c o r e$
FL-GAN	123.25	5.15	174.75	3.95	211.80	3.15
MD-GAN	109.39	6.20	119.51	5.07	149.34	4.12
CAP-GAN	91.97	7.06	97.10	6.73	52.01	9.30
FDA-GAN	75.31	8.99	41.98	9.40	34.61	9.72

Tab. 2 Comparison results of FID and MNIST Score metrics on MNIST dataset

方案	IID		DIR		Fully Non-IID
方案	$V F I D$	$V M N I S T S c o r e$	$V F I D$	$V M N I S T S c o r e$	$V F I D$	$V M N I S T S c o r e$
FL-GAN	123.25	5.15	174.75	3.95	211.80	3.15
MD-GAN	109.39	6.20	119.51	5.07	149.34	4.12
CAP-GAN	91.97	7.06	97.10	6.73	52.01	9.30
FDA-GAN	75.31	8.99	41.98	9.40	34.61	9.72

Tab. 3 Comparison results of Mode Score and MMD metrics on FashionMNIST dataset

方案	IID		DIR		Fully Non-IID
方案	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）
FL-GAN	5.10	5.95	4.97	24.68	4.14	34.17
MD-GAN	7.31	5.24	5.73	13.80	5.77	16.48
CAP-GAN	7.50	1.75	7.18	3.52	8.61	0.87
FDA-GAN	8.37	0.29	8.26	0.96	8.56	0.42

Tab. 3 Comparison results of Mode Score and MMD metrics on FashionMNIST dataset

方案	IID		DIR		Fully Non-IID
方案	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）
FL-GAN	5.10	5.95	4.97	24.68	4.14	34.17
MD-GAN	7.31	5.24	5.73	13.80	5.77	16.48
CAP-GAN	7.50	1.75	7.18	3.52	8.61	0.87
FDA-GAN	8.37	0.29	8.26	0.96	8.56	0.42

Fig. 4 FID metrics of different schemes in three scenarios （MNIST dataset）

Fig. 5 Mode Score metrics of different schemes in three scenarios （FashionMNIST dataset）

Fig. 6 Relationship between communication rounds and model accuracies for different algorithms

Tab. 4 Comparison of model accuracy among different algorithms

算法	MNIST		FashionMNIST
算法	DIR场景	Fully Non-IID场景	DIR场景	Fully Non-IID场景
FDA-GAN+FedAvg	96.23	93.36	82.87	78.51
FDA-GAN+FedProx	95.76	94.09	83.96	78.92
FedAvg	65.54	50.90	50.71	31.86
FedProx	65.40	51.49	50.57	34.50

Tab. 5 Comparison of metrics when the number of participating clients is fixed to 10

方案	$V F I D$	$V M N I S T S c o r e$
FL-GAN	171.45	4.21
MD-GAN	116.72	5.42
CAP-GAN	95.83	6.86
FDA-GAN	38.74	9.60

Tab. 5 Comparison of metrics when the number of participating clients is fixed to 10

方案	$V F I D$	$V M N I S T S c o r e$
FL-GAN	171.45	4.21
MD-GAN	116.72	5.42
CAP-GAN	95.83	6.86
FDA-GAN	38.74	9.60

Tab. 6 Comparison of FID and MNIST Score metrics under different client participation ratios （DIR）

方案	参与比例为0.10		参与比例为0.25		参与比例为0.50		参与比例为0.75
方案	$V F I D$	$V M N I S T S c o r e$	$V F I D$	$V M N I S T S c o r e$	$V F I D$	$V M N I S T S c o r e$	$V F I D$	$V M N I S T S c o r e$
FL-GAN	193.97	3.14	194.75	3.55	174.75	3.95	177.32	4.02
MD-GAN	147.67	3.68	140.17	4.27	119.51	5.07	116.95	5.50
CAP-GAN	114.53	5.81	105.79	6.28	97.10	6.73	96.90	6.65
FDA-GAN	43.01	9.35	42.25	9.44	41.98	9.40	38.63	9.66

Tab. 6 Comparison of FID and MNIST Score metrics under different client participation ratios （DIR）

方案	参与比例为0.10		参与比例为0.25		参与比例为0.50		参与比例为0.75
方案	$V F I D$	$V M N I S T S c o r e$	$V F I D$	$V M N I S T S c o r e$	$V F I D$	$V M N I S T S c o r e$	$V F I D$	$V M N I S T S c o r e$
FL-GAN	193.97	3.14	194.75	3.55	174.75	3.95	177.32	4.02
MD-GAN	147.67	3.68	140.17	4.27	119.51	5.07	116.95	5.50
CAP-GAN	114.53	5.81	105.79	6.28	97.10	6.73	96.90	6.65
FDA-GAN	43.01	9.35	42.25	9.44	41.98	9.40	38.63	9.66

Tab. 7 Comparison of Mode Score and MMD metrics with different numbers of clients （Fully Non-IID）

方案	客户端数为10		客户端数为30		客户端数为40		客户端数为50
方案	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）
FL-GAN	3.81	47.60	4.61	31.19	4.87	27.39	4.90	24.16
MD-GAN	4.76	20.60	5.84	17.33	6.33	14.90	6.43	13.72
CAP-GAN	8.29	0.99	8.66	0.85	8.71	0.85	8.72	0.82
FDA-GAN	8.47	0.51	8.63	0.41	8.69	0.41	8.77	0.40

Tab. 7 Comparison of Mode Score and MMD metrics with different numbers of clients （Fully Non-IID）

方案	客户端数为10		客户端数为30		客户端数为40		客户端数为50
方案	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）	$V M o d e S c o r e$	$V M M D$ （100 $×$ ）
FL-GAN	3.81	47.60	4.61	31.19	4.87	27.39	4.90	24.16
MD-GAN	4.76	20.60	5.84	17.33	6.33	14.90	6.43	13.72
CAP-GAN	8.29	0.99	8.66	0.85	8.71	0.85	8.72	0.82
FDA-GAN	8.47	0.51	8.63	0.41	8.69	0.41	8.77	0.40

Fig. 7 FID and MNIST Score metrics in four ablation experimental scenarios

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Data augmentation scheme based on conditional generative adversarial network in federated learning

联邦学习中基于条件生成对抗网络的数据增强方案

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