Joint Optimization of Edge Selection and Resource Allocation in Digital Twin-assisted Federated Learning
In intelligent driving based on federated learning, the resource constraints of Intelligent Connected Vehicle (ICV) and possible device failures will lead to the decrease of the precision of federated learning training and the increase of delay and energy consumption. Therefore, an optimization scheme of edge selection and resource allocation in digital twin-assisted federated learning is proposed. Firstly, a digital twin-assisted federated learning mechanism is proposed, allowing ICV to choose to participate in federated learning locally or through its digital twin. Secondly, by constructing a computational and communication model for digital twin-assisted federated learning, an edge selection and computing resource allocation joint optimization problem is established with the objective of minimizing cumulative training delay and energy consumption, and is transformed into a partially observable Markov decision process. Finally, an edge selection and resource allocation algorithm based on Multi-agent Parametrized Deep Q-Networks (MPDQN) is proposed to learn approximately optimal edge selection and resource allocation strategies to minimize federated learning cumulative delay and energy consumption. Simulation results show that the proposed algorithm can effectively reduce cumulative training delay and energy consumption of federated learning training while ensuring model accuracy.
万方数据
维普
