Poster: FedWM-Guard: Thwarting Imagination Poisoning in Federated World Model-based Autonomous Driving
A poster proposes FedWM-Guard to stop imagination poisoning of federated autonomous-driving world models.
An arXiv cs.CR poster introduces imagination poisoning against federated world models used for autonomous driving. Compromised vehicles submit bounded updates that preserve benign short-horizon predictions while corrupting long-horizon, including trigger-conditioned, rollouts that can mislead a planner. FedWM-Guard screens authenticated updates with hidden canaries, audits predicted futures against later observations, and invokes a world-model-independent safety shield when inconsistency persists. The authors say evaluation under non-IID data, adaptive attacks, and benign distribution shift is still planned.
- Compromised clients poison long-horizon rollouts while short predictions stay benign.
- FedWM-Guard scores imagined futures instead of update parameters alone.
- Persistent inconsistency triggers a world-model-independent safety shield.
- Evaluation under non-IID data and adaptive attacks is still planned.
From the original article
1 min read at arxiv.orgFederated learning (FL) can improve world model (WM)-based autonomous driving (AD) without centralizing raw private vehicle data, but it also turns model aggregation into a safety-critical integrity boundary.
By Sheng Liu · arxiv.org
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