GHPPFL: A Privacy Preserving Federated Learning Based on Gradient Compression and Homomorphic Encryption in Consumer App Security

Abstract

As Artificial Intelligence (AI) progresses, the application of federated learning in areas such as consumer app security and intelligent transportation systems is increasing rapidly. Federated learning allows model training without necessitating the sharing of local data, yet security issues present obstacles to its advancement. This paper presents a federated learning method that merges gradient compression with homomorphic encryption. Firstly, a unique gradient compression technique is proposed to reduce data transfer by compressing the model parameters exchanged among clients. Then, homomorphic encryption is utilized to prevent breaches of gradient privacy. Experimental results demonstrate that our proposed approach has a minimal impact on the accuracy of the global model, while it reduces data transmission and improves the privacy and security of federated learning.