Internet-of-Things (IoT) devices are often used to transmit physical sensor data over digital wireless channels. Traditional Physical Layer Security (PLS)-based cryptography approaches rely on accurate channel estimation and information exchange for key generation, which irrevocably ties key quality with digital channel estimation quality. Recently, we proposed a new concept called Graph Layer Security (GLS), where digital keys are derived from physical sensor readings. The sensor readings between legitimate users are correlated through a common background infrastructure environment (e.g., a common water distribution network or electric grid). The challenge for GLS has been how to achieve distributed key generation. This paper presents a Federated multi-agent Deep reinforcement learning-assisted Distributed Key generation scheme (FD2K), which fully exploits the common features of physical dynamics to establish secret key between legitimate users. We present for the first time initial experimental results of GLS with federated learning, achieving considerable security performance in terms of key agreement rate (KAR), and key randomness.

翻译：传统物理层安全(PLS)加密方法依赖于关键生成的准确频道估计和信息交流,而关键生成的准确频道估计和信息交流无可挽回地将关键质量与数字频道估计质量联系起来。最近,我们提出了一个名为“图层安全(GLS)”的新概念,其中数字键来自物理传感器读数。合法用户之间的传感器读数通过共同的背景基础设施环境(例如共同的水分配网络或电网)相互关联。GLS面临的挑战是如何实现分布式关键生成。本文介绍了一个联邦多剂深层强化学习辅助配置钥匙生成计划(FD2K),充分利用物理动态的共同特征,在合法用户之间建立秘密钥匙。我们首次介绍了GLS的初始实验结果,该结果通过亲化学习,在关键协议率(KAR)和关键随机性方面实现了相当程度的安全性。</s>