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Proactive edge association is capable of improving wireless connectivity atthe cost of increased handover (HO) frequency and energy consumption, whilerelying on a large amount of private information sharing required for decisionmaking. In order to improve the connectivity-cost trade-off without privacyleakage, we investigate the privacy-preserving joint edge association and powerallocation (JEAPA) problem in the face of the environmental uncertainty and theinfeasibility of individual learning. Upon modelling the problem by adecentralized partially observable Markov Decision Process (Dec-POMDP), it issolved by federated multi-agent reinforcement learning (FMARL) through onlysharing encrypted training data for federatively learning the policy sought.Our simulation results show that the proposed solution strikes a compellingtrade-off, while preserving a higher privacy level than the state-of-the-artsolutions.

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