Federated learning privacy protection scheme based on local differential privacy for remote sensing data

doi:10.11772/j.issn.1001-9081.2024020249

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (2): 506-517.DOI: 10.11772/j.issn.1001-9081.2024020249

• Cyber security • Previous Articles

Federated learning privacy protection scheme based on local differential privacy for remote sensing data

Haitian CHEN¹^,²^,³, Xuebin CHEN¹^,²^,³(), Ruikui MA¹^,², Shuaihua 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:2024-03-11 Revised:2024-04-03 Accepted:2024-04-09 Online:2024-06-04 Published:2025-02-10
Contact: Xuebin CHEN
About author:CHEN Haitian， born in 1998， M. S. candidate. His research interests include data security， privacy protection.
MA Ruikui， born in 1995， M. S. candidate. His research interests include data security， network security.
ZHANG Shuaihua， born in 1999， M. S. candidate. His research interests include data security， network security， privacy protection.
Supported by:
National Natural Science Foundation of China(U20A20179)

面向遥感数据的基于本地差分隐私的联邦学习隐私保护方案

陈海田¹^,²^,³, 陈学斌¹^,²^,³(), 马锐奎¹^,², 张帅华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:陈海田（1998—），男，湖南娄底人，硕士研究生，CCF会员，主要研究方向：数据安全、隐私保护
马锐奎（1995—），男，安徽阜阳人，硕士研究生，CCF会员，主要研究方向：数据安全、网络安全
张帅华（1999—），男，河北石家庄人，硕士研究生，CCF会员，主要研究方向：数据安全、网络安全、隐私保护。
基金资助:
国家自然科学基金资助项目(U20A20179)

Abstract

Abstract:

Remote sensing data have high spatio-temporal correlation and complex surface features， which makes the privacy protection of the data challenging. As a distributed learning method with the goal of protecting data privacy of the participants， federated learning provides an effective solution to overcome the challenges faced by remote sensing data privacy protection. However， during the training phase of federated learning models， malicious attackers may infer private information of the participants through inversion， leading to the disclosure of sensitive information. Aiming at the privacy leakage problem of remote sensing data in federated learning training， a federated learning privacy protection scheme based on local differential privacy was proposed. Firstly， the model was pre-trained， the layer importance of the model was calculated， and the privacy budget was allocated reasonably based on the layer importance. Then， local differential privacy protection was achieved by performing a crop transformation on the model update and performing adaptive random disturbance on the crop value. Finally， model correction was employed to further improve the model performance when the aggregated disturbance was updated. Theoretical analysis and simulation results show that the proposed scheme can not only provide appropriate differential privacy protection for each participant and prevent inferring privacy sensitive information through inversion effectively， but also outperform the segmentation mechanism-based disturbance scheme in accuracy on three remote sensing datasets by 3.28 to 3.93 percentage points. It can be seen that the proposed scheme guarantees model performance effectively while ensuring privacy.

Key words: federated learning, differential privacy, layer importance, remote sensing data, model correction

摘要：

遥感数据具有高度的时空相关性以及复杂的地物特征，使得这些数据的隐私保护面临挑战。联邦学习作为一种旨在保护参与方数据隐私的分布式学习方法，为应对遥感数据隐私保护面对的挑战提供了有效的解决方案；然而，在联邦学习模型的训练阶段，恶意攻击者可能通过反演推断参与者的隐私信息，进而导致敏感信息的泄露。针对遥感数据在联邦学习训练中存在的隐私泄露问题，提出一种基于本地差分隐私的联邦学习隐私保护方案。首先，对模型进行预训练，计算模型的层重要性，并根据层重要性合理分配隐私预算；然后，通过对模型更新进行裁剪变换，并对裁剪值进行自适应随机扰动，实现本地差分隐私保护；最后，在聚合扰动更新时，采用模型校正以进一步提高模型性能。理论分析和仿真结果表明，所提方案不仅能为各参与方提供合适的差分隐私保护，并有效防止通过反演推断出隐私敏感信息，而且在3个遥感数据集上相较于基于分段机制的扰动方案提升了3.28~3.93个百分点的准确率。可见，所提方案在保证隐私的同时有效保障了模型性能。

关键词: 联邦学习, 差分隐私, 层重要性, 遥感数据, 模型校正

CLC Number:

TP309

Haitian CHEN, Xuebin CHEN, Ruikui MA, Shuaihua ZHANG. Federated learning privacy protection scheme based on local differential privacy for remote sensing data[J]. Journal of Computer Applications, 2025, 45(2): 506-517.

陈海田, 陈学斌, 马锐奎, 张帅华. 面向遥感数据的基于本地差分隐私的联邦学习隐私保护方案[J]. 《计算机应用》唯一官方网站, 2025, 45(2): 506-517.

Figures/Tables 19

Fig. 1 Federated learning flow

Fig. 2 Schematic diagram of threat model

Fig. 3 RSLDP-FL framework

Fig. 4 Comparison of worst-case noise variance under different mechanisms

Tab. 1 Remote sensing datasets used in experiments

数据集	图像尺寸	标签类别数	图像数
数据集	图像尺寸	标签类别数	总数	训练集	测试集	验证集
EuroSAT	$64 × 64$	10	27 000	18 900	5 400	2 700
RSSCN7	$400 × 400$	7	2 800	1 960	560	280
WHU-RS19	$600 × 600$	19	1 005	703	202	100
UCMerced	$256 × 256$	21	2 100	1 470	420	210

Tab. 1 Remote sensing datasets used in experiments

数据集	图像尺寸	标签类别数	图像数
数据集	图像尺寸	标签类别数	总数	训练集	测试集	验证集
EuroSAT	$64 × 64$	10	27 000	18 900	5 400	2 700
RSSCN7	$400 × 400$	7	2 800	1 960	560	280
WHU-RS19	$600 × 600$	19	1 005	703	202	100
UCMerced	$256 × 256$	21	2 100	1 470	420	210

Fig. 5 Examples of 10 classes of remote sensing images inEuroSAT dataset

Fig. 6 Examples of 7 classes of remote sensing images inRSSCN7 dataset

Tab. 2 Comparison of defense performance indicators of RSLDP-FL under different disturbance intensities

扰动强度 $σ$	PSNR/dB $↑$	SSIM $↑$	MSE $↓$	LPIPS $↓$
1.00	8.810 7	0.002 6	2.094 2	0.854 1
0.20	9.107 8	0.004 5	1.955 7	0.833 4
0.05	26.708 7	0.272 2	0.033 9	0.436 4
0.02	30.523 9	0.489 9	0.014 1	0.261 1
0.00	33.499 7	0.612 7	0.007 1	0.168 5

Tab. 2 Comparison of defense performance indicators of RSLDP-FL under different disturbance intensities

扰动强度 $σ$	PSNR/dB $↑$	SSIM $↑$	MSE $↓$	LPIPS $↓$
1.00	8.810 7	0.002 6	2.094 2	0.854 1
0.20	9.107 8	0.004 5	1.955 7	0.833 4
0.05	26.708 7	0.272 2	0.033 9	0.436 4
0.02	30.523 9	0.489 9	0.014 1	0.261 1
0.00	33.499 7	0.612 7	0.007 1	0.168 5

Fig. 7 Inversion images of RSLDP-FL under different disturbance intensities

Fig. 8 RSLDP-FL defense effects under different disturbance intensities

Fig. 9 Influence of privacy budget on model performance on different datasets

Fig. 10 Defense performance on different remote sensing images

Tab. 3 Influence of different privacy budget allocation on model performance

分配方式	数据集	$ε T = 1$	$ε T = 5$	$ε T = 8$	$ε T = 10$
自适应分配	EuroSAT	39.61	77.37	88.89	89.74
	WHU-RS19	37.67	75.42	83.57	84.42
	UCMerced	44.35	82.61	89.74	91.19
	RSSCN7	15.51	72.14	83.67	84.82
平均分配	EuroSAT	31.57	69.14	82.11	86.84
	WHU-RS19	30.14	65.28	77.57	81.68
	UCMerced	34.89	72.65	84.48	88.33
	RSSCN7	13.28	59.28	78.42	82.09

Tab. 3 Influence of different privacy budget allocation on model performance

分配方式	数据集	$ε T = 1$	$ε T = 5$	$ε T = 8$	$ε T = 10$
自适应分配	EuroSAT	39.61	77.37	88.89	89.74
	WHU-RS19	37.67	75.42	83.57	84.42
	UCMerced	44.35	82.61	89.74	91.19
	RSSCN7	15.51	72.14	83.67	84.82
平均分配	EuroSAT	31.57	69.14	82.11	86.84
	WHU-RS19	30.14	65.28	77.57	81.68
	UCMerced	34.89	72.65	84.48	88.33
	RSSCN7	13.28	59.28	78.42	82.09

Fig. 11 Influence of number of clients on model performance on different datasets

Fig. 12 Time overhead for different number of clients on each dataset

Fig. 13 Influence of iteration rounds on model performance on different datasets

Tab. 4 Test results of different schemes on different remote sensing datasets

方案	数据集	准确率	精确度	召回率	F1
No-DP	EuroSAT	93.85	93.61	93.47	93.48
	UCMerced	95.71	83.24	83.35	83.29
	RSSCN7	90.25	90.59	90.39	90.49
LDP-FL_D	EuroSAT	23.50	22.81	22.84	22.82
	UCMerced	22.61	13.44	19.38	15.61
	RSSCN7	18.97	19.08	16.01	16.15
LDP-FL_P	EuroSAT	86.46	86.26	85.84	86.04
	UCMerced	87.85	77.25	77.59	77.39
	RSSCN7	80.89	81.14	80.20	81.04
LDP-FL_PS	EuroSAT	88.53	88.26	88.03	88.18
	UCMerced	89.04	77.86	78.46	78.14
	RSSCN7	83.31	84.76	83.61	84.18
RSLDP-FL	EuroSAT	89.74	89.41	89.34	89.37
	UCMerced	91.19	79.25	79.90	79.57
	RSSCN7	84.82	85.18	84.05	85.09

Tab. 5 Comparative results of ablation experiments on different remote sensing datasets

方案	数据集	准确率	精确度	召回率	F1
No-LIC	EuroSAT	86.84	86.60	86.51	86.55
	UCMerced	88.33	77.41	79.04	78.21
	RSSCN7	82.09	83.18	82.57	83.14
No-MC	EuroSAT	86.03	85.48	85.34	85.41
	UCMerced	86.71	76.25	77.19	76.68
	RSSCN7	81.83	82.09	81.25	81.66

Tab. 6 Model performance of RSLDP-FL on MNIST and CIFAR-10 datasets

数据集	算法	准确率/%	模型损失
MNIST	No-DP	92.225	0.316
MNIST	RSLDP-FL	84.112	0.557
CIFAR-10	No-DP	70.599	0.906
CIFAR-10	RSLDP-FL	63.974	1.092

References 43

1	边小勇，费雄君，陈春芳，等. 联合一二阶池化网络学习的遥感场景分类［J］. 计算机应用， 2022， 42（6）： 1972-1978.
	BIAN X Y， FEI X J. CHEN C F，et al. Joint 1-2-order pooling network learning for remote sensing scene classification［J］. Journal of Computer Applications， 2022， 42（6）： 1972-1978.
2	梁敏，汪西莉. 结合超分辨率和域适应的遥感图像语义分割方法［J］. 计算机学报， 2022， 45（12）： 2619-2636.
	LIANG M， WANG X L. Semantic segmentation model for remote sensing images combining super resolution and domain adaptation［J］. Chinese Journal of Computers， 2022， 45（12）： 2619-2636.
3	张省，李山山，魏国芳，等. 面向精细化多尺度特征的遥感图像目标检测［J］. 遥感学报， 2022， 26（12）： 2616-2628.
	ZHANG S， LI S S， WEI G F， et al. Refined multi-scale feature-oriented object detection of the remote sensing images［J］. National Remote Sensing Bulletin， 2022， 26（12）： 2616-2628.
4	ZHU J， WU J， BASHIR A K， et al. Privacy-preserving federated learning of remote sensing image classification with dishonest majority［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2023， 16： 4685-4698.
5	McMAHAN H B， MOORE E， RAMAGE D， et al. Communication-efficient learning of deep networks from decentralized data［C］// Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. New York： JMLR.org， 2017： 1273-1282.
6	王波，李洪涛，王洁，等. 面向医疗数据的隐私保护联邦学习架构［J］. 西安电子科技大学学报， 2023， 50（5）： 166-177.
	WANG B， LI H T， WANG J， et al. Federated learning scheme for privacy-preserving of medical data［J］. Journal of Xidian University， 2023， 50（5）： 166-177.
7	HU H， PENG R， TAI Y W， et al. Network trimming： a data-driven neuron pruning approach towards efficient deep architectures［EB/OL］. ［2023-11-12］..
8	ZHAO Y， ZHAO J， YANG M， et al. Local differential privacy-based federated learning for internet of things［J］. IEEE Internet of Things Journal， 2021， 8（11）： 8836-8853.
9	CHENG G， XIE X， HAN J， et al. Remote sensing image scene classification meets deep learning： challenges， methods， benchmarks， and opportunities［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2020， 13： 3735-3756.
10	屈震，李堃婷，冯志玺. 基于有效通道注意力的遥感图像场景分类［J］. 计算机应用， 2022， 42（5）： 1431-1439.
	QU Z， LI K T， FENG Z X. Remote sensing image scene classification based on effective channel attention［J］. Journal of Computer Applications， 2022， 42（5）： 1431-1439.
11	张昊，何灵敏，潘晨. 改进的SegFormer遥感图像语义分割网络［J］. 计算机工程与应用， 2023， 59（24）： 248-258.
	ZHANG H， HE L M， PAN C. Improved SegFormer remote sensing image semantic segmentation network［J］. Computer Engineering and Applications， 2023， 59（24）： 248-258.
12	SONG Q， CAO J， LI Y， et al. On efficient federated learning for aerial remote sensing image classification： a filter pruning approach［C］// Proceedings of the 2023 International Conference on Neural Information Processing， LNCS 14450. Singapore： Springer， 2024： 184-199.
13	SHIVKUMAR S， PALANISWAMY S. Satellite image segmentation using federated learning： a privacy preserving approach［C］// Proceedings of the 7th International Conference on Computation System and Information Technology for Sustainable Solutions. Piscataway： IEEE， 2023： 1-6.
14	ZHANG X， ZHANG B， YU W， et al. Federated deep learning with prototype matching for object extraction from very-high-resolution remote sensing images［J］. IEEE Transactions on Geoscience and Remote Sensing， 2023， 61： No.5603316.
15	陈学斌，任志强，张宏扬. 联邦学习中的安全威胁与防御措施综述［J］. 计算机应用， 2024， 44（6）：1663-1672.
	CHEN X B， REN Z Q， ZHANG H Y. Review on security threats and defense measures in federated learning［J］. Journal of Computer Applications， 2024， 44（6）：1663-1672.
16	段昕汝，陈桂茸，陈爱网，等. 联邦学习中的信息安全问题研究综述［J］. 计算机工程与应用， 2024， 60（3）： 61-77.
	DUAN X R， CHEN G R， CHEN A W， et al. Review of research on information security in federated learning［J］. Computer Engineering and Applications， 2024， 60（3）： 61-77.
17	NASR M， SHOKRI R， HOUMANSADR A. Comprehensive privacy analysis of deep learning： passive and active white-box inference attacks against centralized and federated learning［C］// Proceedings of the 2019 IEEE Symposium on Security and Privacy. Piscataway： IEEE， 2019： 739-753.
18	ZHAO B， MOPURI K R， BILEN H. iDLG： improved deep leakage from gradients［EB/OL］. ［2023-11-12］..
19	ZHU L， LIU Z， HAN S. Deep leakage from gradients［C］// Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2019： 14774-14784.
20	SONG M， WANG Z， ZHANG Z， et al. Analyzing user-level privacy attack against federated learning［J］. IEEE Journal on Selected Areas in Communications， 2020， 38（10）： 2430-2444.
21	张佳乐，朱诚诚，孙小兵，等. 基于GAN的联邦学习成员推理攻击与防御方法［J］. 通信学报， 2023， 44（5）： 193-205.
	ZHANG J L， ZHU C C， SUN X B， et al. Membership inference attack and defense method in federated learning based on GAN［J］. Journal on Communications， 2023， 44（5）： 193-205.
22	高莹，陈晓峰，张一余，等. 联邦学习系统攻击与防御技术研究综述［J］. 计算机学报， 2023， 46（9）： 1781-1805.
	GAO Y， CHEN X F， ZHANG Y Y， et al. A review of research on attack and defense techniques for federated learning systems［J］. Chinese Journal of Computers， 2023， 46（9）： 1781-1805.
23	WANG T， ZHANG Y， JIA R. Improving robustness to model inversion attacks via mutual information regularization［C］// Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto： AAAI Press， 2021： 11666-11673.
24	PENG X， LIU F， ZHANG J， et al. Bilateral dependency optimization： defending against model-inversion attacks［C］// Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York： ACM， 2022： 1358-1367.
25	SHEJWALKAR V， HOUMANSADR A. Membership privacy for machine learning models through knowledge transfer［C］// Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto： AAAI Press， 2021： 9549-9557.
26	周辉，陈玉玲，王学伟，等. 基于生成对抗网络的联邦学习深度影子防御方案［J］. 计算机应用， 2024， 44（1）： 223-232.
	ZHOU H， CHEN Y L， WANG X W， et al. Deep shadow defense scheme of federated learning based on generative adversarial network［J］. Journal of Computer Applications， 2024， 44（1）： 223-232.
27	LUO X， ZHANG X. Exploiting defenses against GAN-based feature inference attacks in federated learning［EB/OL］. ［2024-10-12］..
28	JEGOROVA M， KAUL C， MAYOR C， et al. Survey： leakage and privacy at inference time［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2023， 45（7）： 9090-9108.
29	LIU Y， WEN R， HE X， et al. ML-Doctor： holistic risk assessment of inference attacks against machine learning models［C］// Proceedings of the 31st USENIX Security Symposium. Berkeley： USENIX Association， 2022： 4525-4542.
30	张连福，谭作文. 一种面向多模态医疗数据的联邦学习隐私保护方法［J］. 计算机科学， 2023， 50（11A）： No.230800021.
	ZHANG L F， TAN Z W. Federated learning privacy-preserving approach for multimodal medical data［J］. Computer Science， 2023， 50（11A）： No.230800021.
31	于雅娜，李红娇，李晋国. 差分隐私保护WGAN-GP算法研究［J］. 计算机应用研究， 2021， 38（9）： 2837-2841.
	YU Y N， LI H J， LI J G. Research on differential privacy protection for WGAN-GP algorithm［J］. Application Research of Computers， 2021， 38（9）： 2837-2841.
32	ZHANG Z， MA X， MS J. Local differential privacy based membership-privacy-preserving federated learning for deep-learning-driven remote sensing［J］. Remote Sensing， 2023， 15（20）： No.5050.
33	DUAN Q， HU S， DENG R， et al. Combined federated and split learning in edge computing for ubiquitous intelligence in internet of things： state-of-the-art and future directions［J］. Sensors， 2022， 22（16）： No.5983.
34	罗长银，王君宇，陈学斌，等. 改进的联邦加权平均算法［J］. 计算机应用， 2022， 42（4）： 1131-1136.
	LUO C Y， WANG J Y， CHEN X B， et al. Improved federated weighted average algorithm［J］. Journal of Computer Applications， 2022， 42（4）： 1131-1136.
35	INAN A， GURSOY M E， SAYGIN Y. Sensitivity analysis for non-interactive differential privacy： bounds and efficient algorithms［J］. IEEE Transactions on Dependable and Secure Computing， 2020， 17（1）： 194-207.
36	尹春勇，屈锐. 基于个性化差分隐私的联邦学习算法［J］. 计算机应用， 2023， 43（4）： 1160-1168.
	YIN C Y， QU Y. Federated learning algorithm based on personalized differential privacy［J］. Journal of Computer Applications， 2023， 43（4）： 1160-1168.
37	张国鹏，陈学斌，王豪石，等. 面向本地差分隐私的K-Prototypes聚类方法［J］. 计算机应用， 2022， 42（12）： 3813-3821.
	ZHANG G P， CHEN X B， WANG H S， et al. K-Prototypes clustering method for local differential privacy［J］. Journal of Computer Applications， 2022， 42（12）： 3813-3821.
38	DUCHI J C， JORDAN M I， WAINWRIGHT M J. Minimax optimal procedures for locally private estimation［J］. Journal of the American Statistical Association， 2018， 113（521）： 182-201.
39	WANG N， XIAO X， YANG Y， et al. Collecting and analyzing multidimensional data with local differential privacy［C］// Proceedings of the IEEE 35th International Conference on Data Engineering. Piscataway： IEEE， 2019： 638-649.
40	HELBER P， BISCHKE B， DENGEL A， et al. EuroSAT： a novel dataset and deep learning benchmark for land use and land cover classification［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2019， 12（7）： 2217-2226.
41	芦国军，陈丽芳. 基于深度卷积神经网络的遥感图像场景分类［J］. 太原师范学院学报（自然科学版）， 2019， 18（1）：57-62.
	LU G J， CHEN L F. Remote sensing image classification based on deep convolution neural network［J］. Journal of Taiyuan Normal University （Natural Science Edition）， 2019， 18（1）：57-62.
42	DAI D， YANG W. Satellite image classification via two-layer sparse coding with biased image representation［J］. IEEE Geoscience and Remote Sensing Letters， 2011， 8（1）：173-176.
43	DIMITROVSKI I， KITANOVSKI I， KOCEV D， et al. Current trends in deep learning for earth observation： an open-source benchmark arena for image classification［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2023， 197： 18-35.

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Federated learning privacy protection scheme based on local differential privacy for remote sensing data

面向遥感数据的基于本地差分隐私的联邦学习隐私保护方案

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