E2EGI: End-to-End Gradient Inversion in Federated Learning

A plethora of healthcare data is produced every day due to the proliferation of prominent technologies such as Internet of Medical Things (IoMT). Digital-driven smart devices like wearable watches, wristbands and bracelets are utilized extensively in modern healthcare applications. Mining valuable information from the data distributed at the owners' level is useful, but it is challenging to preserve data privacy. Federated learning (FL) has swiftly surged in popularity due to its efficacy in dealing privacy vulnerabilities. Recent studies have demonstrated that Gradient Inversion Attack (GIA) can reconstruct the input data by leaked gradients, previous work demonstrated the achievement of GIA in very limited scenarios, such as the label repetition rate of the target sample being low and batch sizes being smaller than 48. In this paper, a novel method of End-to-End Gradient Inversion (E2EGI) is proposed. Compared to the state-of-the-art method, E2EGI's Minimum Loss Combinatorial Optimization (MLCO) has the ability to realize reconstructed samples with higher similarity, and the Distributed Gradient Inversion algorithm can implement GIA with batch sizes of 8 to 256 on deep network models (such as ResNet-50) and ImageNet datasets. A new Label Reconstruction algorithm is developed that relies only on the gradient information of the target model, which can achieve a label reconstruction accuracy of 81% in one batch sample with a label repetition rate of 96%, a 27% improvement over the state-of-the-art method. This proposed work can underpin data security assessments for healthcare federated learning.

Automated summary

This paper presents a novel approach called End-to-End Gradient Inversion (E2EGI) to address the challenge of mining valuable information from owners' level data while preserving data privacy. Compared to existing methods, E2EGI has the ability to reconstruct samples with higher similarity and can implement the Distributed Gradient Inversion algorithm with batch sizes of 8 to 256 on deep network models (such as ResNet-50) and ImageNet datasets. Additionally, a new Label Reconstruction algorithm is developed that achieves an 81% label reconstruction accuracy in one batch sample with a label repetition rate of 96%, a 27% improvement over existing methods. This work can support data security assessments for healthcare federated learning.