Federated Learning Meets Contract Theory: Economic-Efficiency Framework for Electric Vehicle Networks

In this paper, we propose a novel economic-efficiency framework for an electric vehicle (EV) network to maximize the profits (i.e., amount of money that can be earned) charging stations (CSs). To end, first introduce energy demand prediction method CSs leveraging federated learning approaches, in which each CS train its own transactions locally and exchange learned model with other improve quality while protecting CS's information privacy. Based on predicted demands, reserve from smart grid provider (SGP) advance optimize profit. Nonetheless, due competition among as well unknown SGP, i.e., willingness transfer energy, develop multi-principal one-agent (MPOA) contract-based address these issues. particular, formulate CSs’ profit maximization non-collaborative contract problem under SGP's common constraints contracts. solve problem, transform it into equivalent low-complexity optimization iterative algorithm find optimal contracts CSs. Through simulation results using real dataset, demonstrate our proposed enhance accuracy up 24.63 percent compared machine algorithms. Furthermore, outperform economic models by 48 36 terms utilities social welfare total all participating entities) network, respectively.