Abstract: Edge intelligent computing is widely used in the fields of Internet of things (IoT), industrial control UAV cluster and so on, which has the advantages of high data processing efficiency, strong real-time performance and low network delay. However, there are many problems when edge intelligent device, edge gateways and cloud complete the task unloading, scheduling and coordination. For example, there are problems that are privacy disclosure, limited calculation force. As is known to all, federated learning allows all training devices to complete training in parallel, which greatly improve training efficiency. However, traditional federated learning will expose the edge device’s information of the training set. So, this article propose a privacy protection scheme combining edge intelligent computing and federated learning (PPCEF). First of all, we propose a lightweight privacy protection protocol based on sharing secret and weight mask, which is based on a random mask scheme of secret sharing. It can not only protect gradient privacy without losing model accuracy, but also resist equipment dropping and collusion attacks between devices, which has strong practicability. Secondly, we design an algorithm based on digital signature and hash function, which can not only achieve the integrity and consistency of the message, but also resist replay attacks. Finally, we use MNIST and CIFAR 10 data sets to prove that our scheme is safe in practice.