We introduce LegalEdge, an edge intelligence-driven framework that integrates Federated Learning (FL) and Deep Q-Networks (DQN) to optimize electric vehicle (EV) charging infrastructure. LegalEdge contracts are novel smart contracts deployed on the blockchain to manage dynamic pricing and incentive mechanisms transparently and autonomously. By leveraging FL, multiple edge devices such as EV charging stations collaboratively train DQN agents without sharing raw data, preserving user privacy while reducing communication costs. These edge-deployed agents learn optimal charging strategies in real time based on local conditions and global policy updates. LegalEdge ensures low-latency decisions, high contract integrity, and efficient energy allocation. Our experimental results demonstrate significant improvements in learning convergence, transaction speed, and operational transparency, establishing LegalEdge as a scalable, intelligent, and accountable solution for next-generation EV charging networks.

翻译：本文提出LegalEdge，一种集成联邦学习与深度Q网络的边缘智能框架，旨在优化电动汽车充电基础设施。LegalEdge合约是部署于区块链的新型智能合约，通过透明自主的方式管理动态定价与激励机制。该框架利用联邦学习技术，使多个边缘设备（如电动汽车充电站）能够在不共享原始数据的情况下协同训练深度Q网络智能体，在保护用户隐私的同时降低通信成本。这些部署于边缘的智能体可根据本地状态与全局策略更新实时学习最优充电策略。LegalEdge系统确保了低延迟决策、高合约完整性与高效能源分配。实验结果表明，该方法在学习收敛速度、交易处理效率与运营透明度方面均有显著提升，证明LegalEdge可成为新一代电动汽车充电网络的可扩展、智能化且具备问责机制的解决方案。