ESMFL: Efficient and Secure Models for Federated Learning
This addresses privacy concerns and communication costs in distributed machine learning systems, though it appears incremental by combining existing techniques like SGX and sparsification.
The paper tackles privacy and communication bandwidth issues in federated learning by proposing a privacy-preserving method using Intel SGX for security and sparsification to reduce transmission overhead, achieving reasonable accuracy across different model architectures.
Nowadays, Deep Neural Networks are widely applied to various domains. However, massive data collection required for deep neural network reveals the potential privacy issues and also consumes large mounts of communication bandwidth. To address these problems, we propose a privacy-preserving method for the federated learning distributed system, operated on Intel Software Guard Extensions, a set of instructions that increase the security of application code and data. Meanwhile, the encrypted models make the transmission overhead larger. Hence, we reduce the commutation cost by sparsification and it can achieve reasonable accuracy with different model architectures.