More and more Internet of Things (IoT) applications provide intelligent services, with the development of artificial intelligence algorithms, such as deep reinforcement learning. However, along with the trend of utilizing a large model with high accuracy in AI-enabled IoT, resource-limited IoT devices are difficult to handle these large-scale models with high response latency. By collaborating with edge nodes, the devices could respond quickly. However, IoT applications contain a large amount of user privacy, and pushing data to others might lead to privacy leakage. Inspired by the trusted execution environment technology, we propose a framework that enables trusted collaboration for future AI-enabled IoTs, in terms of computation security and transmission security, where the data could be processed in an isolated environment, and two approaches are proposed to ensure the security in data transmission. Experimental results show that our framework provides flexible and dynamic collaboration with low overhead and can effectively support collaborative edge intelligence.

随着人工智能算法（例如深度强化学习）的发展，越来越多的物联网（IoT）应用程序提供智能服务。但是，随着在具有AI功能的IoT中使用具有高精度的大型模型的趋势，资源受限的IoT设备很难处理具有高响应延迟的大规模模型。通过与边缘节点协作，设备可以快速响应。但是，物联网应用程序包含大量用户隐私，将数据推送给其他人可能会导致隐私泄漏。受受信任的执行环境技术的启发，我们提出了一个框架，该框架可以在计算安全性和传输安全性方面为未来的支持AI的IoT提供可靠的协作，其中可以在隔离的环境中处理数据，提出了两种方法来保证数据传输的安全性。实验结果表明，我们的框架以较低的开销提供了灵活，动态的协作，并且可以有效地支持协作边缘智能。