Page 810 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

differential privacy levels and incorporating multi-faceted privacy protection: for instance,
combining differential privacy with secure aggregation and encryption so that even if some
infrastructure is compromised, individual vehicle data remains protected. We will also define
clear privacy metrics to monitor and ensure compliance with data protection regulations. In
parallel, research into communication-efficient FL will continue. Ultimately, by leveraging these
advanced federated learning approaches, the system aims to learn from vast distributed data
in a privacy-preserving manner, resulting in highly accurate models that no single entity could
have trained alone. Success will be measured by demonstrating that a model trained in this
federated network achieves performance on par with a hypothetically centrally-trained model,
as well as by meeting targets for communication efficiency and privacy.

V2X Security and System Robustness: A major long-term focus will be hardening the entire
system against security threats and failure modes, especially as it transitions to real-world critical
infrastructure. This entails enhancing V2X system security on multiple fronts. All vehicle-to-
infrastructure and vehicle-to-vehicle communications will employ state-of-the-art encryption,
authentication, and integrity-checking in line with automotive standards. For instance,
messages exchanged will be signed and verified to prevent spoofing, and sensitive updates
in the federated learning process will utilize secure aggregation to thwart eavesdroppers. The
digital twin platform can be utilized to simulate cyber-attacks to test the system’s resilience
and to refine intrusion detection algorithms. Additionally, the interplay of LLMs introduces
new considerations for robustness: measures will be taken to prevent and mitigate issues like
malicious or ambiguous inputs to the LLM, and to ensure the LLM’s outputs do not compromise
safety. By incorporating these defensive techniques and continuously validating them, the
system is expected to maintain a high level of trust and safety. The end-state vision is an
intelligent mobility network where autonomous agents cooperate seamlessly using learned
intelligence, and where both data privacy and communication security are upheld by design.
This will instill confidence that the transition from theoretical models to real-world operation
does not introduce vulnerabilities, thus paving the way for widespread adoption of the
technology.

3 Use Case Requirements

• REQ-01: It is mandatory that the system operates on a standard 4G network, as the
transmitted data consists of lightweight text tokens; however, it is also critical that
network coverage exceeds 95% to avoid service disruptions and ensure a continuous
user experience.
• REQ-02: It is critical that the vehicle is equipped with a Service-Oriented Architecture
(SOA), with in-vehicle functions modularized into atomic services to enable seamless
orchestration.
• REQ-03: It is mandatory that the system supports API interfaces for large language model
cloud services from certified providers, ensuring legal compliance, high stability, and
scalability to handle varying concurrency levels.

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