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This summary is machine-generated.

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.

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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.