  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.
