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.
Abstract (translated)
主动边缘连接可以在提高无线连接的同时,增加 handover(HO)频率和能源消耗,而只需要依靠决策所需的大量私人信息分享。为了在不泄露隐私的情况下改善连接成本权衡,我们研究在环境不确定性和个人学习可行性限制下,保持隐私的联合边缘连接和功率分配(JEAPA)问题。通过采用分布式可观察的马氏决策过程( Dec-POMDP)来建模问题,联邦多代理强化学习(FMARL)通过仅分享加密的训练数据来分布式学习旨在采取的政策。我们的模拟结果显示,提出的解决方案实现了令人瞩目的权衡,同时保留了比当前解决方案更高的隐私水平。
URL
https://arxiv.org/abs/2301.11014
