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.

针对面临环境不确定性和无法进行个体学习的情况，研究了在保障隐私的前提下联合边缘关联和功率分配问题。通过联邦多智能体强化学习来解决这一问题，提高了无线连接性的成本效益，保护了更高的隐私级别，同时只共享加密的训练数据。