HAFLO: GPU-Based Acceleration for Federated Logistic Regression
This work addresses computational inefficiencies in privacy-preserving federated learning, which is crucial for industries using decentralized data, though it is incremental as it optimizes existing methods.
The paper tackles the performance bottleneck in federated logistic regression caused by homomorphic encryption by proposing HAFLO, a GPU-based acceleration solution that achieves speedups of 49.9x for heterogeneous and 88.4x for homogeneous logistic regression.
In recent years, federated learning (FL) has been widely applied for supporting decentralized collaborative learning scenarios. Among existing FL models, federated logistic regression (FLR) is a widely used statistic model and has been used in various industries. To ensure data security and user privacy, FLR leverages homomorphic encryption (HE) to protect the exchanged data among different collaborative parties. However, HE introduces significant computational overhead (i.e., the cost of data encryption/decryption and calculation over encrypted data), which eventually becomes the performance bottleneck of the whole system. In this paper, we propose HAFLO, a GPU-based solution to improve the performance of FLR. The core idea of HAFLO is to summarize a set of performance-critical homomorphic operators (HO) used by FLR and accelerate the execution of these operators through a joint optimization of storage, IO, and computation. The preliminary results show that our acceleration on FATE, a popular FL framework, achieves a 49.9$\times$ speedup for heterogeneous LR and 88.4$\times$ for homogeneous LR.