Trustworthy AI for 6G-IoV: A Privacy-Preserved Distributed Multiagent Federated DRL for Dynamic Electric Vehicle Charging and Task Offloading

As electric vehicles (EVs) join the 6G Internet of Vehicles (6G-IoV), they must charge while moving and process data in real time. Static plug-in charging and on-board CPUs cannot meet these dual demands. We therefore combine two technologies. First, wireless power transfer (WPT) on electrified roads (eROAD) keeps an EV’s battery topped up throughout its trip. Second, a privacy-preserved multi-agent federated deep-RL (MA-FDRL) framework decides, every slot, where each task runs—on the vehicle, a roadside unit (RSU), or the base station (BS). Federated learning keeps raw data local; differential privacy protects model updates; deep reinforcement learning allocates radio, compute, and energy resources for many agents in real time. Experiments show the scheme cuts task-offloading latency, balances edge-server load, and scales gracefully with network size. The outcome suggests an energy-conscious architecture capable of meeting the low-latency and privacy needs of 6G-IoV.