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Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy
Yuan Tian1, Yanfeng Shi1, Yue Zhang1
1School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China.
Sensors (Basel, Switzerland)
|January 11, 2025
Summary
This study introduces a personalized federated learning method with correlated differential privacy for autonomous driving. It enhances data privacy and utility while accommodating user differences, offering a refined solution for secure data sharing.
Area of Science:
- Computer Science
- Artificial Intelligence
- Cybersecurity
Background:
- Big data and smart sensors are prevalent, necessitating secure data sharing.
- Data privacy concerns are significant, especially in applications like autonomous driving.
- Existing methods often struggle with user heterogeneity and data utility.
Purpose of the Study:
- To propose a personalized federated learning method for autonomous driving.
- To integrate correlated differential privacy for enhanced data protection.
- To address limitations of traditional differential privacy in customized scenarios.
Main Methods:
- Federated learning is employed for decentralized model training at each node.
- Correlated classification analysis is used to encrypt highly relevant data, minimizing system costs.
- Correlated differential privacy is applied to preserve data privacy before sharing.
Main Results:
- The proposed scheme offers enhanced privacy tailored to individual user needs.
- Experimental results demonstrate superior refinement in handling user heterogeneity.
- The method improves data utility without compromising privacy.
Conclusions:
- The personalized federated learning with correlated differential privacy is effective for autonomous driving.
- This approach provides a more customized and robust solution for data privacy preservation.
- It balances the need for data utility with stringent privacy requirements.
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