Abstract: The use of distributed deployment of sensors will lead to the edge of the server data distribution single imbalance phenomenon, the model training under edge computing can also result in serious privacy leakage problem due to the data set pollution caused by gradient anomaly. RDSEC(Strongly Robust Data Security Algorithms for Edge Computing) is proposed, encryption algorithm is used to encrypt the parameters of the edge server to protect privacy. If an anomaly is found in the gradient anomaly detection of the edge node, the edge node uploads the gradient with a signal to tell the cloud center if the current parameters uploaded by the edge node are available. The experimental results on CIFAR10 and Fashion data sets show that the algorithm can efficiently aggregate the parameters of edge servers and improve the computing power and accuracy of edge nodes. Under the condition of ensuring data privacy, the robustness, accuracy and training speed of the model are greatly improved, and the high accuracy of edge node is achieved. 

Key words: sensor, edge server, privacy leakage, parameter sharing