FedPurifier: Proactive Distributed Experience Purification for Resilient Connected Autonomous Vehicle Platoons

Multi-agent reinforcement learning and graph attention networks are widely applied to cooperative control in connected autonomous vehicle platoons. However, anomalous experiences from local sensor faults or backdoor attacks propagate to neighbors via inter-vehicle communication and infiltrate the global policy through parameter sharing, triggering cascading platoon collisions. In conventional solutions, pre-filtering corrupted data struggles to detect stealthy anomalies, rigid behavioral constraints induce coordination deadlocks, and retraining from scratch incurs prohibitive computational and time costs. To address these challenges, we propose FedPurifier, an experience purification framework tailored for connected vehicle platoons. While maintaining fleet-wide parameter sharing, FedPurifier introduces orthogonal communication signatures to pinpoint responsible vehicles upon collision, performs contrastive unlearning across dual action and communication spaces to eliminate anomalous experiences, and preserves nominal driving capabilities via parameter anchoring. Across four MetaDrive benchmarks with 100 independent random seeds, FedPurifier on average reduces the backdoor attack success rate to 6.6% and restores the safety rate to 61.9%. The purified policy matches complete retraining performance while accelerating computation by two to three orders of magnitude.