Personalized federated learning method based on model pre-assignment and self-distillation

doi:10.11772/j.issn.1001-9081.2025010115

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (1): 10-20.DOI: 10.11772/j.issn.1001-9081.2025010115

• Artificial intelligence • Previous Articles Next Articles

Personalized federated learning method based on model pre-assignment and self-distillation

Kejia ZHANG¹, Zhijun FANG¹^,²(), Nanrun ZHOU¹, Zhicai SHI³

^1.School of Electronic and Electrical Engineering，Shanghai University of Engineering Science，Shanghai 201620，China
^2.School of Computer Science and Technology，Donghua University，Shanghai 201620，China
^3.School of Information Engineering，Shanghai Zhongqiao Vocational and Technical University，Shanghai 201514，China

Received:2025-02-07 Revised:2025-03-14 Accepted:2025-03-19 Online:2026-01-10 Published:2026-01-10
Contact: Zhijun FANG
About author:ZHANG Kejia， born in 2001， M. S. candidate. His research interests include federated learning.
ZHOU Nanrun， born in 1976， Ph. D.， professor. His research interests include information and communication engineering， cyberspace security.
SHI Zhicai， born in 1964， Ph. D.， professor. His research interests include embedded system， computer network， information security.
Supported by:
Scientific and Technological Innovation 2030-Major Project of “New Generation of Artificial Intelligence”(2020AAA0109300);Shanghai Municipal Science and Technology Commission Local College Capacity Building Project(23010501800)

基于模型预分配与自蒸馏的个性化联邦学习方法

张珂嘉¹, 方志军¹^,²(), 周南润¹, 史志才³

^1.上海工程技术大学电子电气工程学院，上海 201620
^2.东华大学计算机科学与技术学院，上海 201620
^3.上海中侨职业技术大学信息工程学院，上海 201514

通讯作者: 方志军
作者简介:张珂嘉（2001—），男，河南新乡人，硕士研究生，主要研究方向：联邦学习
周南润（1976—），男，江西吉安人，教授，博士，主要研究方向：信息与通信工程、网络空间安全
史志才（1964—），男，吉林磐石人，教授，博士，主要研究方向：嵌入式系统、计算机网络、信息安全。
基金资助:
科技创新2030—“新一代人工智能”重大项目(2020AAA0109300);上海市科委地方院校能力建设项目(23010501800)

Abstract

Abstract:

Federated Learning （FL） is a distributed machine learning method that utilizes distributed data for model training while ensuring data privacy. However， it performs poorly in scenarios with highly heterogeneous data distributions. Personalized Federated Learning （PFL） addresses this challenge by providing personalized models for each client. However， the previous PFL algorithms primarily focus on optimizing client local models， while ignoring optimization of server global model. Consequently， server computational resources are not utilized fully. To overcome these limitations，FedPASD， a PFL method based on model pre-assignment and self-distillation， was proposed. FedPASD was operated in both server-side and client-side aspects. On server-side， client models for the next round were pre-assigned targetedly， which not only enhanced model personalization performance， but also utilized server computational resources effectively. On client-side， models were trained hierarchically and fine-tuned using self-distillation to better adapt to local data distribution characteristics. Experimental results on three datasets， CIFAR-10，Fashion-MNIST， and CIFAR-100 of comparing FedPASD with classic algorithms such as FedCP （Federated Conditional Policy），FedPAC （Personalization with feature Alignment and classifier Collaboration）， and FedALA （Federated learning with Adaptive Local Aggregation） as baselines demonstrate that FedPASD achieves higher test accuracy than those of baseline algorithms under various data heterogeneity settings. Specifically，FedPASD achieves a test accuracy improvement of 29.05 to 29.22 percentage points over traditional FL algorithms and outperforms the PFL algorithms by 1.11 to 20.99 percentage points on CIFAR-100 dataset； on CIFAR-10 dataset，FedPASD achieves a maximum accuracy of 88.54%.

Key words: Federated Learning (FL), data heterogeneity, Personalized Federated Learning (PFL), model pre-assignment, self-distillation

摘要：

联邦学习（FL）是一种分布式机器学习方法，即利用分布式数据在训练模型的同时保护数据隐私。然而，它在高度异构的数据分布情况时表现不佳。个性化联邦学习（PFL）通过为每个客户端提供个性化模型来解决这一问题。然而，以往的PFL算法主要侧重于客户端本地模型的优化，忽略了服务器端全局模型的优化，导致服务器计算资源没有得到充分利用。针对上述局限性，提出基于模型预分配（PA）与自蒸馏（SD）的PFL方法FedPASD。FedPASD从服务器端和客户端2方面入手：在服务器端，对下一轮客户端模型有针对性地预先分配，这样不仅能提高模型的个性化性能，还能有效利用服务器的计算能力；在客户端，经过分层训练，并通过模型自蒸馏微调使模型更好地适应本地数据分布的特点。在3个数据集CIFAR-10、Fashion-MNIST和CIFAR-100上，将FedPASD与FedCP （Federated Conditional Policy）、FedPAC （Personalization with feature Alignment and classifier Collaboration）和FedALA （Federated learning with Adaptive Local Aggregation）等作为基准的典型算法进行对比实验的结果表明：FedPASD在不同异构设置下的测试准确率都高于基准算法。具体而言，FedPASD在CIFAR-100数据集上，客户端数量为50，参与率为50%的实验设置中，测试准确率较传统FL算法提升了29.05~29.22个百分点，较PFL算法提升了1.11~20.99个百分点；在CIFAR-10数据集上最高可达88.54%测试准确率。

关键词: 联邦学习, 数据异构, 个性化联邦学习, 模型预分配, 自蒸馏

CLC Number:

TP181

Kejia ZHANG, Zhijun FANG, Nanrun ZHOU, Zhicai SHI. Personalized federated learning method based on model pre-assignment and self-distillation[J]. Journal of Computer Applications, 2026, 46(1): 10-20.

张珂嘉, 方志军, 周南润, 史志才. 基于模型预分配与自蒸馏的个性化联邦学习方法[J]. 《计算机应用》唯一官方网站, 2026, 46(1): 10-20.

Figures/Tables 14

Fig. 1 Overall architecture of FedPASD

Fig. 2 Server-side pre-assignment architecture of models

Fig. 3 Local training process of client model

Tab. 1 Dataset information

数据集	任务	样本数	图片大小	类别数
CIFAR-10	图像分类	60 000	32 $×$ 32	10
Fashion-MNIST	图像分类	70 000	28 $×$ 28	10
CIFAR-100	图像分类	60 000	32 $×$ 32	100

Tab. 1 Dataset information

数据集	任务	样本数	图片大小	类别数
CIFAR-10	图像分类	60 000	32 $×$ 32	10
Fashion-MNIST	图像分类	70 000	28 $×$ 28	10
CIFAR-100	图像分类	60 000	32 $×$ 32	100

Fig. 4 Distribution of data samples across clients on CIFAR-10 dataset

Tab. 2 Test accuracy comparison of different algorithms on three datasets under different client settings

算法	CIFAR-10		Fashion-MNIST		CIFAR-100
算法	20clients	50clients	20clients	50clients	20clients	50clients
FedAvg^［1］	39.34	38.22	78.80	75.46	20.47	12.40
FedProx^［14］	39.16	40.14	78.98	75.07	20.35	12.56
FedLC^［16］	39.14	40.03	79.00	75.89	20.34	12.39
FedBN^［9］	39.17	38.63	78.79	75.91	20.45	12.41
FedCP^［29］	87.76	83.58	96.32	95.35	46.68	39.44
FedFomo^［31］	87.52	81.60	96.49	94.81	38.09	35.69
FedProto^［33］	86.04	80.39	96.20	93.78	45.52	27.62
FedPAC^［12］	84.03	81.83	95.66	94.50	48.00	39.22
FedALA^［35］	88.14	83.96	96.34	95.46	45.60	37.92
FedGH^［39］	88.00	83.84	96.49	95.56	45.96	40.50
FedAS^［40］	85.84	81.26	96.61	95.64	24.51	20.62
FedPASD	88.54	84.30	96.73	95.55	50.42	41.61

Fig. 5 Convergence curves of each algorithm under different client settings

Tab. 3 Communication rounds required for different algorithms to achieve target accuracy

算法	CIFAR-10		Fashion-MNIST		CIFAR-100
算法	轮次	提速倍数	轮次	提速倍数	轮次	提速倍数
FedProto^［33］	200	1.00	200	1.00	200	1.00
FedFomo^［31］	54	3.70	158	1.27	—	—
FedPAC^［12］	—	—	—	—	160	1.25
FedCP^［29］	77	2.60	182	1.10	181	1.10
FedALA^［35］	63	3.17	185	1.09	200	1.00
FedGH^［39］	74	2.70	156	1.60	194	1.03
FedAS^［40］	120	1.67	105	1.90	—	—
FedPASD	48	4.17	135	1.48	136	1.47

Tab. 4 AUC metrics of different algorithms under default experimental setting

算法	CIFAR-10	Fashion-MNIST	CIFAR-100
FedAvg^［1］	0.800 3	0.943 3	0.806 0
FedProx^［14］	0.798 5	0.943 6	0.805 7
FedLC^［16］	0.798 1	0.943 0	0.806 3
FedBN^［9］	0.798 9	0.943 9	0.805 6
FedCP^［29］	0.985 3	0.9980	0.964 7
FedFomo^［31］	0.977 7	0.994 9	0.949 5
FedProto^［33］	0.984 5	0.997 6	0.965 2
FedPAC^［12］	0.944 3	0.990 5	0.961 9
FedALA^［35］	0.892 1	0.975 5	0.952 1
FedGH^［39］	0.968 5	0.971 5	0.908 2
FedAS^［40］	0.980 0	0.993 4	0.938 1
FedPASD	0.9856	0.996 5	0.9724

Tab. 5 Test accuracy comparison of each algorithm on three datasets under different degrees of data heterogeneity

算法	CIFAR-10				Fashion-MNIST				CIFAR-100
算法	$α$ =0.1	$α$ =0.3	$α$ =0.5	$α$ =0.8	$α$ =0.1	$α$ =0.3	$α$ =0.5	$α$ =0.8	$α$ =0.1	$α$ =0.3	$α$ =0.5	$α$ =0.8
FedAvg^［1］	39.34	47.31	52.07	52.92	78.80	83.37	86.00	85.76	20.47	22.27	22.89	23.05
FedProx^［14］	39.16	47.64	52.39	52.59	78.98	83.67	86.06	85.70	20.35	22.06	22.88	22.97
FedLC^［16］	39.14	47.53	52.16	52.71	79.00	83.54	85.80	85.80	20.34	22.08	22.86	22.93
FedBN^［9］	39.17	47.64	52.38	52.56	78.79	83.44	85.86	85.85	20.45	22.07	22.84	22.86
FedCP^［29］	87.76	76.46	70.59	65.76	96.32	91.77	90.02	89.02	46.68	36.15	30.23	25.68
FedFomo^［31］	87.52	76.22	67.04	61.69	96.49	91.44	88.65	87.06	38.09	29.14	23.23	19.80
FedProto^［33］	86.04	73.77	65.17	60.69	96.20	89.77	86.30	84.91	45.52	32.92	27.59	23.69
FedPAC^［12］	84.03	77.04	71.75	67.12	95.66	91.83	90.28	89.40	48.00	37.01	32.23	28.66
FedALA^［35］	88.14	77.45	71.87	67.41	96.34	92.15	90.46	89.46	45.60	37.77	33.80	29.50
FedGH^［39］	88.00	76.52	71.16	66.92	96.49	92.12	90.53	89.44	45.96	36.19	31.85	28.01
FedAS^［40］	85.84	70.01	59.66	52.92	96.61	91.87	89.21	87.71	24.51	17.16	11.92	6.83
FedPASD	88.54	77.96	72.38	67.60	96.73	92.36	90.50	89.55	50.42	39.26	33.86	29.30

Tab. 5 Test accuracy comparison of each algorithm on three datasets under different degrees of data heterogeneity

算法	CIFAR-10				Fashion-MNIST				CIFAR-100
算法	$α$ =0.1	$α$ =0.3	$α$ =0.5	$α$ =0.8	$α$ =0.1	$α$ =0.3	$α$ =0.5	$α$ =0.8	$α$ =0.1	$α$ =0.3	$α$ =0.5	$α$ =0.8
FedAvg^［1］	39.34	47.31	52.07	52.92	78.80	83.37	86.00	85.76	20.47	22.27	22.89	23.05
FedProx^［14］	39.16	47.64	52.39	52.59	78.98	83.67	86.06	85.70	20.35	22.06	22.88	22.97
FedLC^［16］	39.14	47.53	52.16	52.71	79.00	83.54	85.80	85.80	20.34	22.08	22.86	22.93
FedBN^［9］	39.17	47.64	52.38	52.56	78.79	83.44	85.86	85.85	20.45	22.07	22.84	22.86
FedCP^［29］	87.76	76.46	70.59	65.76	96.32	91.77	90.02	89.02	46.68	36.15	30.23	25.68
FedFomo^［31］	87.52	76.22	67.04	61.69	96.49	91.44	88.65	87.06	38.09	29.14	23.23	19.80
FedProto^［33］	86.04	73.77	65.17	60.69	96.20	89.77	86.30	84.91	45.52	32.92	27.59	23.69
FedPAC^［12］	84.03	77.04	71.75	67.12	95.66	91.83	90.28	89.40	48.00	37.01	32.23	28.66
FedALA^［35］	88.14	77.45	71.87	67.41	96.34	92.15	90.46	89.46	45.60	37.77	33.80	29.50
FedGH^［39］	88.00	76.52	71.16	66.92	96.49	92.12	90.53	89.44	45.96	36.19	31.85	28.01
FedAS^［40］	85.84	70.01	59.66	52.92	96.61	91.87	89.21	87.71	24.51	17.16	11.92	6.83
FedPASD	88.54	77.96	72.38	67.60	96.73	92.36	90.50	89.55	50.42	39.26	33.86	29.30

Tab. 6 Test accuracy comparison of different algorithms on CIFAR-10 and CIFAR-100 datasets under different client participation rates

算法	CIFAR-10			CIFAR-100
算法	$r$ = 30%	$r$ = 50%	$r$ = 80%	$r$ = 30%	$r$ = 50%	$r$ = 80%
FedAvg^［1］	38.03	38.22	40.34	11.90	12.40	12.71
FedProx^［14］	38.01	40.14	40.21	12.11	12.56	12.87
FedLC^［16］	39.17	40.03	40.01	12.15	12.39	12.75
FedBN^［9］	37.42	38.63	39.74	12.15	12.41	12.67
FedCP^［29］	83.26	83.58	83.90	38.36	39.44	40.27
FedFomo^［31］	80.25	81.60	82.22	32.76	35.69	37.14
FedProto^［33］	78.91	80.39	81.13	18.83	27.62	37.20
FedPAC^［12］	82.94	81.83	79.22	38.31	39.22	40.01
FedALA^［35］	83.15	83.96	83.93	37.97	37.92	37.10
FedGH^［39］	83.74	83.84	83.96	39.82	40.50	40.69
FedAS^［40］	81.67	81.26	80.70	20.14	20.62	23.20
FedPASD	83.72	84.30	84.64	40.05	41.61	43.51

Tab. 6 Test accuracy comparison of different algorithms on CIFAR-10 and CIFAR-100 datasets under different client participation rates

算法	CIFAR-10			CIFAR-100
算法	$r$ = 30%	$r$ = 50%	$r$ = 80%	$r$ = 30%	$r$ = 50%	$r$ = 80%
FedAvg^［1］	38.03	38.22	40.34	11.90	12.40	12.71
FedProx^［14］	38.01	40.14	40.21	12.11	12.56	12.87
FedLC^［16］	39.17	40.03	40.01	12.15	12.39	12.75
FedBN^［9］	37.42	38.63	39.74	12.15	12.41	12.67
FedCP^［29］	83.26	83.58	83.90	38.36	39.44	40.27
FedFomo^［31］	80.25	81.60	82.22	32.76	35.69	37.14
FedProto^［33］	78.91	80.39	81.13	18.83	27.62	37.20
FedPAC^［12］	82.94	81.83	79.22	38.31	39.22	40.01
FedALA^［35］	83.15	83.96	83.93	37.97	37.92	37.10
FedGH^［39］	83.74	83.84	83.96	39.82	40.50	40.69
FedAS^［40］	81.67	81.26	80.70	20.14	20.62	23.20
FedPASD	83.72	84.30	84.64	40.05	41.61	43.51

Tab. 7 Impact of different hyperparameter μ value on algorithm test accuracy

$μ$	测试准确率/%
$μ$	CIFAR-10	Fashion-MNIST	CIFAR-100
0.0	88.19	96.60	50.23
0.2	88.29	96.61	50.27
0.5	88.54	96.73	50.42
0.8	88.62	96.64	50.39

Tab. 7 Impact of different hyperparameter μ value on algorithm test accuracy

$μ$	测试准确率/%
$μ$	CIFAR-10	Fashion-MNIST	CIFAR-100
0.0	88.19	96.60	50.23
0.2	88.29	96.61	50.27
0.5	88.54	96.73	50.42
0.8	88.62	96.64	50.39

Tab. 8 Impact of different hyperparameter τ value on algorithm test accuracy

$τ$	测试准确率/%
$τ$	CIFAR-10	Fashion-MNIST	CIFAR-100
2	88.54	96.73	50.42
5	88.32	96.68	50.67
8	88.40	96.65	50.49
10	88.22	96.70	50.38

Tab. 8 Impact of different hyperparameter τ value on algorithm test accuracy

$τ$	测试准确率/%
$τ$	CIFAR-10	Fashion-MNIST	CIFAR-100
2	88.54	96.73	50.42
5	88.32	96.68	50.67
8	88.40	96.65	50.49
10	88.22	96.70	50.38

Tab. 9 Impact of different modules on algorithm test accuracy

模块	测试准确率/%
模块	CIFAR-10	Fashion-MNIST	CIFAR-100
None	85.77	95.66	45.52
SD	88.24	96.64	50.10
PA	86.28	96.02	48.49
Both	88.54	96.73	50.42

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Personalized federated learning method based on model pre-assignment and self-distillation

基于模型预分配与自蒸馏的个性化联邦学习方法

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