FPGA versus GPU for accelerating homomorphic encryption in federated learning

Yuan Liu; Zixiao Wang; Biyao Che; Ying Chen; Jizhuang Zhao

doi:10.1117/12.3014890

8 December 2023 FPGA versus GPU for accelerating homomorphic encryption in federated learning

Yuan Liu, Zixiao Wang, Biyao Che, Ying Chen, Jizhuang Zhao

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 1294311 (2023) https://doi.org/10.1117/12.3014890
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

As a new learning paradigm preserving data privacy in distributed machine learning， federated learning becomes increasingly attractive，in which Homomorphic Encryption (HE) technology is utilized to encrypt the private intermediate data during computation. However, the homomorphic operation in HE impose significant computing overhead on federated learning. A hardware solution is important to speed up the training process in federated learning. As current two popular hardware accelerating platform, both GPU based and FPGA based accelerators are introduced into this area, but which is a better choice? We device and customize a FPGA implementation of the homomorphic encryption, as well as a GPU version. The experiment results demonstrate that GPU is more efficient for PHE computations in most case, because GPU version outperforms its counterpart on performance in terms of throughputs. However in some cases FPGA version achieves better performance than the GPU, with far lower clock frequency.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuan Liu, Zixiao Wang, Biyao Che, Ying Chen, and Jizhuang Zhao "FPGA versus GPU for accelerating homomorphic encryption in federated learning", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 1294311 (8 December 2023); https://doi.org/10.1117/12.3014890

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