Towards High-performance Hardware Acceleration for Cross-silo Federated Learning

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


MPhil Thesis Defence


Title: "Towards High-performance Hardware Acceleration for Cross-silo Federated
Learning"

By

Mr. Xiaodian CHENG


Abstract:

In recent years, public concern over privacy security has led to an increasing
emphasis on privacy preservation. Cross-silo Federated Learning (FL) has been
applied in both academia and industry to connect data holders who are isolated
by laws and regulations. However, to guarantee data security, considerable
overhead is introduced by the security protocols in FL because of the high
calculation complexity and large data size, preventing FL from being efficient
in real-life applications.

This thesis proposes hardware acceleration designs that target the
high-performance computation of nine widely used cryptographic operations in
FL, including homomorphic encryption and RSA algorithm. Compared to traditional
CPU approaches, our hardware designs leverage the abundant computation and
storage resources on hardware devices such as GPU, FPGA, and ASIC. Our solution
consists of two parts. First, we present the GPU-based acceleration design,
HAFLO, for federated logistic regression. This design enables mainstream FL
frameworks to better utilize GPU devices. Second, we propose FLASH, a specially
designed hardware acceleration architecture for FL. FLASH accelerates
cryptographic operations with fully pipelined computation engines and the data
flow scheduling module. We implement FLASH on the VU13P FPGA for prototyping
and conduct performance assessment for the ASIC design of FLASH. Our evaluation
results show that the FPGA prototype achieves 6.8× and 2.0× speedups for FL
applications over CPU and GPU, respectively. The ASIC design of FLASH further
achieves 23.6× acceleration over the FPGA prototype.


Date:                   Tuesday, 20 June 2023

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                        lifts 25/26

Committee Members:      Prof. Kai Chen (Supervisor)
                        Dr. Minhao Cheng (Chairperson)
                        Dr. Songze Li (EMIA)


**** ALL are Welcome ****