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.

翻译：主动式边缘关联能够以增加切换频率和能耗为代价改善无线连接性能，同时需要共享大量决策所需的隐私信息。为在不泄露隐私的前提下优化连接与成本之间的权衡，我们研究了环境不确定性和个体学习不可行性场景下兼具隐私保护的联合边缘关联与功率分配问题。通过将该问题建模为分布式部分可观测马尔可夫决策过程，采用联邦多智能体强化学习仅共享加密训练数据以联邦学习所需策略进行求解。仿真结果表明，所提方案在实现良好性能权衡的同时，相较于现有先进方案具有更高的隐私保护水平。