Combating Knowledge Diversity and Catastrophic Forgetting in UAV-Assisted Collaborative Vehicular Learning: A Game-Theoretic Approach

Knowledge diversity and catastrophic forgetting are two significant challenges in sustaining long-term collaborative learning within heterogeneous vehicle networks. These challenges become more notable when vehicles have varying sensing capabilities, computational resources, and domain expertise, which can result in fragmented learning and unstable knowledge retention. To tackle these issues, we propose a dual-level game-theoretic framework for UAV-assisted heterogeneous vehicle networks. We introduce a new metric called Utility-of-Information (UoI) that quantifies knowledge diversity, retention, and consolidation in a comprehensive manner. The framework comprises a lower-level coalition formation game, where vehicles self-organize into “teacher-student” coalitions based on their UoI profiles, and an upper-level UAV resource allocation game, where vehicle coalitions compete for limited communication resources. To solve the associated resource optimization problem under dynamic conditions, we develop a unified reinforcement learning framework utilizing Proximal Policy Optimization (PPO). Extensive simulations and testbed validations demonstrate that the proposed approach effectively manages knowledge diversity, mitigates catastrophic forgetting, and supports long-term collaborative learning in real-world UAV-assisted vehicle networks.