Edge Intelligence-Driven LegalEdge Contracts for EV Charging Stations: A Fedrated Learning with Deep Q-Networks Approach

TL;DR

LegalEdge introduces a blockchain-based smart contract framework utilizing federated learning and deep reinforcement learning to optimize EV charging, ensuring privacy, transparency, and real-time decision-making in decentralized energy networks.

Contribution

It presents a novel integration of federated learning, blockchain smart contracts, and deep Q-networks for decentralized EV charging management, enhancing privacy and operational efficiency.

Findings

Improved convergence speed of learning agents.

Enhanced transaction speed and contract transparency.

Effective real-time energy allocation and decision-making.

Abstract

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.