Data Security Sharing Method for the Internet of Things Based on Federated Incremental Learning
In order to improve the accuracy of IoT data security sharing transmission and shorten the time required for IoT data secu-rity sharing,this study proposes a federated incremental learning-based IoT data security sharing method.It constructs an IoT data sharing model,improves federated learning through class increment,updates the parameters of the data sharing model,and effec-tively avoids local optimization problems.It generates a sharing request to SN,matches access control permissions based on updated model parameters,calculates IoT data security sharing keys,recovers target data content,and implements IoT data security sharing methods.The experimental results show that when the interference intensity is 60dB,the accuracy of secure sharing transmission of IoT data in this method is 99.1%.When the amount of IoT data is 300GB,the time for secure sharing of IoT data in this method is only 0.9s,indicating that this method can effectively improve the security sharing effect of IoT data and improve the efficiency of IoT data security sharing.
federated incremental learningInternet of Things datasecure sharingcloud server
NETL
NSTL
万方数据
