Internet of things data security and privacy protection based on improved federated learning

Internet of Things (IoT) and more new technologies have brought great convenience to daily life, but they have also raised more data security and privacy protection. To ensure the security of IoT data and user privacy in various scenarios, the federated learning (FL) method is optimized based on function encryption and blockchain. At the same time, using the decentralized function to encrypt the privacy of the training model, the learning model can provide more secure and reliable services, aiming to solve the problem of large difference in the quality of computing nodes and data privacy leakage in the current FL. The experimental results show that the model accuracy of the optimized system reaches 93%, which is significantly higher than 90% of the traditional centralized model. In addition, the improved approach effectively reduced the risk of data breaches by 1.5%, increased the resistance to attacks by 90%, and increased the user trust by 85%. The average response time of the optimized system is between 5 and 100 milliseconds. When the data dimension is less than 32 and the size of the data terminal group is less than 10, the terminal traffic of the improved FL data protection system is less than that of the other two systems. When the data terminal group was 50, the calculated communication traffic was 19,008 bits, which was an acceptable range for improving the data protection system of FL. Experimental results demonstrate that the improved method can improve the accuracy of the learning model and ensure the efficiency of data processing while protecting user privacy in data sharing.