Distributed Layer-Partitioned Training for Privacy-Preserved Deep Learning
This addresses privacy concerns for users who need to train deep learning models on sensitive data in cloud environments, though it appears incremental.
The paper tackles the problem of protecting sensitive data in cloud-based deep learning by proposing a distributed layer-partitioned training method with step-wise activation functions, achieving effectiveness in privacy preservation as demonstrated experimentally.
Deep Learning techniques have achieved remarkable results in many domains. Often, training deep learning models requires large datasets, which may require sensitive information to be uploaded to the cloud to accelerate training. To adequately protect sensitive information, we propose distributed layer-partitioned training with step-wise activation functions for privacy-preserving deep learning. Experimental results attest our method to be simple and effective.