Secure, Privacy Preserving, and Verifiable Federating Learning Using Blockchain for Internet of Vehicles
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Internet of Vehicles (IoV) has been sought as a solution to realize an Intelligent Transportation System (ITS) for efficient traffic management. Data driven ITS requires learning from vehicular data and provide vehicles with timely information to support a wide range of safety and infotainment ITS applications. IoV is vulnerable to multitude of cyber-attacks and privacy concerns. Feder-ated Learning (FL) is on the verge of delivering the collab-orative learning by exchanging learning model parameters instead of actual data which is expected to provide privacy in IoV. However, despite featuring an inherently secure and privacy-preserving framework, FL is still vulnerable to poisoning and reverse engineering attacks. Blockchain technology (BC) has already demonstrated a zero-trust, fully secure, distributed, and auditable information recording and sharing paradigm. In this paper, we present a practical prospect of blockchain empowered federated learning to realize fully secure, privacy preserving and verifiable FL for the IoV that is capable of providing secure and trustworthy ITS services.