Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning

TL;DR

This paper proposes a privacy-preserving federated multi-agent reinforcement learning approach to optimize edge association and power allocation in the Internet of Vehicles, balancing connectivity and privacy without sacrificing performance.

Contribution

It introduces a decentralized partially observable Markov Decision Process model and a federated learning framework for joint optimization with privacy guarantees.

Findings

Achieves better privacy preservation than existing solutions.

Balances connectivity and energy efficiency effectively.

Demonstrates superior performance through simulations.

Abstract

Proactive edge association is capable of improving wireless connectivity at the cost of increased handover (HO) frequency and energy consumption, while relying on a large amount of private information sharing required for decision making. In order to improve the connectivity-cost trade-off without privacy leakage, we investigate the privacy-preserving joint edge association and power allocation (JEAPA) problem in the face of the environmental uncertainty and the infeasibility of individual learning. Upon modelling the problem by a decentralized partially observable Markov Decision Process (Dec-POMDP), it is solved by federated multi-agent reinforcement learning (FMARL) through only sharing encrypted training data for federatively learning the policy sought. Our simulation results show that the proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.