Privacy-Preserving Data Pipelines for Federated Learning in Autonomous Driving with Microservices
DOI:
https://doi.org/10.15662/IJRAI.2023.0601003Keywords:
Privacy-preserving, Data pipelines, Federated learning, Autonomous driving, Microservices architecture, Secure aggregation, Differential privacy, Edge computing, Trustworthy AI, Real-time decision makingAbstract
The rapid adoption of autonomous driving technologies generates massive volumes of sensitive data that require secure and efficient processing. Traditional centralized machine learning approaches pose significant privacy risks, as raw data transmission from vehicles to cloud infrastructures can expose personal and location-sensitive information. To address this challenge, this paper proposes a privacy-preserving data pipeline for federated learning (FL) in autonomous driving, implemented through a microservices-based architecture. The framework enables distributed training directly on edge devices while only sharing model updates, thereby mitigating data leakage risks. Each microservice is modularly designed to handle specific tasks such as secure data ingestion, feature engineering, encryption, differential privacy, and secure aggregation. The architecture supports scalability, interoperability, and real-time adaptability, ensuring robust communication between vehicles, roadside units, and cloud servers. Experimental validation demonstrates that the proposed system not only enhances privacy and security but also maintains high model accuracy and low latency for decision-making tasks critical to autonomous navigation. This research contributes to advancing trustworthy AI in autonomous driving by integrating federated learning, privacy-preserving techniques, and microservices engineering into a cohesive and practical pipeline.
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