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This work proposes a practical framework for verifiable DP training using CPU-side TEEs together with untrusted GPUs, and addresses a fundamental efficiency-security tension: training entirely inside a CPU TEE is too slow, while unrestricted GPU offloading can allow malicious deviations from DP.
A novel hierarchical framework enables autonomous vehicles to anticipate long-term traffic dynamics while making real-time decisions, achieving superior driving performance.
