Quantum-Driven Context-Aware Federated Learning in Heterogeneous Vehicular Metaverse Ecosystem

TL;DR: This study introduces QV-MetaFL, a quantum-based federated learning framework for vehicular metaverse, addressing heterogeneity and communication costs with Q-STP and Q-VCG, and a composite loss function, demonstrating transformative capabilities through comprehensive simulations.

Abstract: In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-MetaFL, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-MetaFL is strengthened by the quantum sequential-training-program (Q-STP) algorithm, a quantum-based sequential training program that transforms model training, reducing communication costs and adeptly managing vehicle states. Complementing this, the quantum vehicle-context-grouping (Q-VCG) mechanism groups vehicles based on contextual data similarity, effectively tackling the complexities of data heterogeneity. The synergy of Q-STP and Q-VCG culminates in the QV-MetaFL algorithm, a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Additionally, our research introduces an innovative composite loss function that amalgamates classical loss metrics with quantum parameter regularization, deftly addressing quantum sensitivity to noise. The effectiveness of the QV-MetaFL framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.