Federated learning algorithm for personalization and fairness

doi:10.11772/j.issn.1001-9081.2024070934

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (7): 2123-2131.DOI: 10.11772/j.issn.1001-9081.2024070934

• The 39th CCF National Conference of Computer Applications (CCF NCCA 2024) • Previous Articles Next Articles

Federated learning algorithm for personalization and fairness

Hongyang ZHANG¹^,²^,³, Shufen ZHANG¹^,²^,⁴(), Zheng GU¹^,²^,³

^1.College of Sciences，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
^4.Tangshan Key Laboratory of Big Data Security and Intelligent Computing （North China University of Science and Technology），Tangshan Hebei 063210，China

Received:2024-07-05 Revised:2024-09-25 Accepted:2024-09-29 Online:2025-07-10 Published:2025-07-10
Contact: Shufen ZHANG
About author:ZHANG Hongyang， born in 1999， M. S. candidate. His research interests include data security， privacy protection.
ZHANG Shufen， born in 1972， M. S.， professor. Her research interests include cloud computing， data security， privacy protection.
GU Zheng， born in 1999， M. S. candidate. Her research interests include data analysis.
Supported by:
National Natural Science Foundation of China(U20A20179)

面向个性化与公平性的联邦学习算法

张宏扬¹^,²^,³, 张淑芬¹^,²^,⁴(), 谷铮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
^4.唐山市大数据安全与智能计算重点实验室（华北理工大学），河北唐山 063210

通讯作者: 张淑芬
作者简介:张宏扬（1999—），男，江苏淮安人，硕士研究生，CCF会员，主要研究方向：数据安全、隐私保护
张淑芬（1972—），女，河北唐山人，教授，硕士，CCF高级会员，主要研究方向：云计算、数据安全、隐私保护 zhsf@ncst.edu.cn
谷铮（1999—），女，河北廊坊人，硕士研究生，CCF会员，主要研究方向：数据分析。
基金资助:
国家自然科学基金资助项目(U20A20179)

Abstract

Abstract:

As a distributed optimization paradigm， Federated Learning （FL） enables a large number of resource-constrained client nodes to train models collaboratively without sharing the data. However， traditional federated learning algorithms， such as fedAvg， often fail to address fairness issues adequately. In practical scenarios， data distributions are highly heterogeneous typically， and conventional aggregation operations may introduce model biases towards certain clients， resulting in significant local performance disparities across clients of the global model. To tackle this challenge， a federated learning algorithm for personalization and fairness named FedPF （Federated learning for Personalization and Fairness） was proposed. FedPF aims to reduce inefficient aggregation behaviors in federated learning effectively， and distribute personalized models among clients by exploring the correlations between the global model and local models， thereby ensuring a balanced performance distribution among clients while maintaining performance of the global model. FedPF was evaluated and analyzed on Synthetic， MNIST， and CIFAR10 datasets， and was compared with three federated learning algorithms： FedProx， q-FedAvg， and FedAvg. Experimental results demonstrate that FedPF achieves notable improvements in both effectiveness and fairness.

Key words: federated learning, fairness, personalization, heterogeneous data, client selection

摘要：

作为一种分布式优化范式，联邦学习（FL）允许大量资源有限的客户端节点在不共享数据时协同训练模型。然而，传统联邦学习算法，如FedAvg，通常未充分考虑公平性的问题。在实际场景中，数据分布通常具备高度异构性，常规的聚合操作可能会使模型对某些客户端产生偏见，导致全局模型在客户端本地的性能分布出现巨大差异。针对这一问题，提出一种面向个性化与公平性的联邦学习FedPF（Federated learning for Personalization and Fairness）算法。FedPF旨在有效减少联邦学习中低效的聚合行为，并通过寻找全局模型与本地模型的相关性，在客户端之间分配个性化模型，从而在保证全局模型性能的同时，使客户端本地性能分布更均衡。将FedPF在Synthetic、MNIST以及CIFAR10数据集上进行实验和性能分析，并与FedProx、q-FedAvg和FedAvg这3种联邦学习算法进行对比。实验结果表明，FedPF在有效性和公平性上均得到了有效提升。

关键词: 联邦学习, 公平, 个性化, 异构数据, 客户端选择

CLC Number:

TP309.2

Hongyang ZHANG, Shufen ZHANG, Zheng GU. Federated learning algorithm for personalization and fairness[J]. Journal of Computer Applications, 2025, 45(7): 2123-2131.

张宏扬, 张淑芬, 谷铮. 面向个性化与公平性的联邦学习算法[J]. 《计算机应用》唯一官方网站, 2025, 45(7): 2123-2131.

Figures/Tables 14

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算法	TA/%	TL	MVA/%	MVL	SDVA	SDVL
FedAvg	68.16	0.891	70.09	0.842	0.324	0.719
FedProx	69.27	0.919	70.98	0.910	0.326	1.156
q-FedAvg	51.83	1.613	53.92	1.588	0.387	0.604
FedPF	80.16	0.720	82.25	0.675	0.170	0.281

算法	TA/%	TL	MVA/%	MVL	SDVA	SDVL
FedAvg	68.16	0.891	70.09	0.842	0.324	0.719
FedProx	69.27	0.919	70.98	0.910	0.326	1.156
q-FedAvg	51.83	1.613	53.92	1.588	0.387	0.604
FedPF	80.16	0.720	82.25	0.675	0.170	0.281

数据集	算法	TA/%	TL	MVA/%	MVL	SDVA	SDVL
MNIST （Diversity）	FedAvg	97.81	0.067	97.50	0.083	0.021	0.062
	FedProx	97.06	0.087	97.01	0.101	0.022	0.062
	q-FedAvg	91.85	0.587	90.93	0.609	0.043	0.125
	FedPF	98.08	0.058	97.67	0.074	0.021	0.050
MNIST （Dirichlet）	FedAvg	97.63	0.072	97.18	0.091	0.025	0.073
	FedProx	96.09	0.112	95.35	0.136	0.076	0.199
	q-FedAvg	89.92	0.665	88.64	0.693	0.062	0.159
	FedPF	97.63	0.070	97.19	0.085	0.021	0.055

数据集	算法	TA/%	TL	MVA/%	MVL	SDVA	SDVL
MNIST （Diversity）	FedAvg	97.81	0.067	97.50	0.083	0.021	0.062
	FedProx	97.06	0.087	97.01	0.101	0.022	0.062
	q-FedAvg	91.85	0.587	90.93	0.609	0.043	0.125
	FedPF	98.08	0.058	97.67	0.074	0.021	0.050
MNIST （Dirichlet）	FedAvg	97.63	0.072	97.18	0.091	0.025	0.073
	FedProx	96.09	0.112	95.35	0.136	0.076	0.199
	q-FedAvg	89.92	0.665	88.64	0.693	0.062	0.159
	FedPF	97.63	0.070	97.19	0.085	0.021	0.055

数据集	算法	TA/%	TL	MVA/%	MVL	SDVA	SDVL
CIFAR10 （Diversity）	FedAvg	59.99	1.359	59.92	1.364	0.114	0.437
	FedProx	65.65	1.103	65.37	1.115	0.104	0.355
	q-FedAvg	41.95	1.649	42.99	1.645	0.100	0.130
	FedPF	64.35	1.162	64.54	1.135	0.120	0.363
CIFAR10 （Dirichlet）	FedAvg	41.26	1.617	40.07	1.643	0.268	0.664
	FedProx	52.89	1.345	53.52	1.354	0.186	0.530
	q-FedAvg	39.49	1.641	38.90	1.671	0.160	0.223
	FedPF	58.09	1.252	57.11	1.257	0.197	0.594

Federated learning algorithm for personalization and fairness

面向个性化与公平性的联邦学习算法

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