Privacy-Preserving Aggregation for Federated Learning-Based Navigation in Vehicular Fog

Kong, Qinglei; Yin, Feng; Lu, Rongxing; Li, Beibei; Wang, Xiaohong; Cui, Shuguang; Zhang, Ping

doi:10.1109/tii.2021.3075683

Cited by 71 publications

(26 citation statements)

References 22 publications

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“…In Table Ⅱ, the comparison between our scheme and other related schemes [15,17,18,20,21] shows that our scheme does not require any trusted entity, can resist all attacks, meets the privacy protection requirements, and realizes reverse attack resistance.…”

Section: Feature Comparisonmentioning

confidence: 99%

“…Kong et al [20] proposed a privacy-preserving model aggregation scheme based on a federated learning navigation framework. A homomorphic threshold cryptosystem combines the skip list and the bounded Laplace mechanism to protect the locally trained model updates.…”

Section: Related Workmentioning

confidence: 99%

“…A federated extreme gradient boosting learning algorithm is constructed to solve the data isolation problem, and a data sharing mechanism is designed to ensure secure on-demand control data sharing. Scheme [20] relies on the trusted authority (TA), and scheme [21] assumes that the Key generation center (KGC) is the trusted entity. In fact, a trusted entity does not exist, and there is a risk of information source leakage, so it is not practical.…”

Section: Related Workmentioning

confidence: 99%

“…To facilitate the comparison of FLPDA scheme with schemes [18] and [20], the execution time of each algorithm is listed in Table Ⅲ.…”

Section: A Computation Complexitymentioning

confidence: 99%

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Federated Learning-Based Privacy-Preserving Data Aggregation Scheme for IIoT

2023

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The Industrial Internet of Things (IIoT) is the key technology of Industry 4.0. The combination of machine learning and IIoT has spawned a thriving smart industry. Machine learning models are trained and predicted based on raw data that contains sensitive information, and data sharing leads to information leakage. Data security and privacy protection in IIoT face serious challenges. Therefore, we propose a federated learning-based privacy-preserving data aggregation scheme (FLPDA) for IIoT. Data aggregation to protect individual user model changes in federated learning against reverse analysis attacks from industry administration centers. Each round of data aggregation uses the PBFT consensus algorithm to select an IIoT device from the aggregation area as the initialization and aggregation node. Paillier cryptosystem and secret sharing are combined to realize data fault tolerance and secure sharing. Security analysis and performance evaluation show that the scheme can effectively protect data privacy and resist various attacks. It has lower communication, computational, and storage overhead than existing schemes.

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Section: Feature Comparisonmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…To facilitate the comparison of FLPDA scheme with schemes [18] and [20], the execution time of each algorithm is listed in Table Ⅲ.…”

Section: A Computation Complexitymentioning

confidence: 99%

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Federated Learning-Based Privacy-Preserving Data Aggregation Scheme for IIoT

2023

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“…In addition, they also use a local differential privacy algorithm to mask personal data. In [53], the authors propose a different method to tackle this problem. They introduce a privacypreserving model aggregation scheme named FedLoc by using homomorphic encryption and a bounded Laplace mechanism.…”

Section: Deep Learning For Data Securitymentioning

confidence: 99%

The Current Trends of Deep Learning in Autonomous Vehicles: A Review

Huang¹,

Ren²

2022

J. Engg. Res. & Sci.

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Autonomous vehicles are the future of road traffic. In addition to improving safety and efficiency from reduced errors compared to conventional vehicles, autonomous vehicles can also be implemented in applications that may be inconvenient or dangerous to a human driver. To realize this vision, seven essential technologies need to be evolved and refined including path planning, computer vision, sensor fusion, data security, fault diagnosis, control, and lastly, communication and networking. The contributions and the novelty of this paper are: 1) provide a comprehensive review of the recent advances in using deep learning for autonomous vehicle research, 2) offer insights into several important aspects of this emerging area, and 3) identify five directions for future research. To the best of our knowledge, there is no previous work that provides similar reviews for autonomous vehicle design.

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Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

Lakshminarayanan,

Dhanasekaran,

Vinod Kumar

et al. 2024

Int J Communication

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SummaryWireless sensor networks (WSNs) require precise node location in order to function properly, and they are essential in many different applications. In this research, we propose a unique method to maximize the accurate estimate of average localization errors (ALEs) in WSNs by utilizing federated learning (FL) approaches. The suggested approach combines the extraction of spatial features with collection of contextual data through integration of hybrid convolutional neural network–bidirectional encoder representations from transformers (CNN‐BERT) model. Effectively applying min–max normalization to input features minimizes data from flowing into test, validation, and training sets. The technique is centered around a collaborative learning architecture, wherein the weights of the model are iteratively assessed and modified on centralized server. The suggested methodology's effectiveness in an array of settings is illustrated by mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) values for node localization in WSN. Network B demonstrated accuracy with an MAE of 0.05, MSE of 0.03, and RMSE of 0.10; Network C demonstrated strong accuracy with an MAE of 0.09, MSE of 0.08, and RMSE of 0.12; and Network A generated an MAE of 0.04, MSE of 0.06, and RMSE of 0.08. Furthermore, the centralized server, which is crucial for collaborative learning, obtained exceptional MAE of 0.01, MSE of 0.02, and RMSE of 0.99 demonstrating the superiority of the FL‐optimized method in improving localization accuracy. The outcomes demonstrate the FL‐optimized hybrid model's superiority over conventional methods in providing accurate node localization in a variety of WSN settings.

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Privacy-Preserving Aggregation for Federated Learning-Based Navigation in Vehicular Fog

Cited by 71 publications

References 22 publications

Federated Learning-Based Privacy-Preserving Data Aggregation Scheme for IIoT

Federated Learning-Based Privacy-Preserving Data Aggregation Scheme for IIoT

The Current Trends of Deep Learning in Autonomous Vehicles: A Review

Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

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