Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning. (arXiv:2301.11014v1 [cs.LG])

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 …

agent connectivity cost decision decision making edge energy information information sharing internet large making optimization order power privacy private proactive problem sharing trade vehicles wireless