Accelerating Multi-Party Computation Using Heterogeneous Systems
Xiteng Yao, Shining Yang, Mayank Varia, Martin Herbordt · 2025
Multi-party computation (MPC) allows multiple parties to compute with private data without sharing it. We have previously found that MPC through Secret Sharing has some advantages in data center deployments. But while MPC provides strong privacy guarantees, in Secret Sharing MPC, the protocol’s communication between parties often takes a significant portion of the total execution time. That is, adding MPC to applications like neural network training can increase the execution time from minutes to days. A significant fraction of this increased execution time is due to the use of generic TCP/IP networks and other communication overhead.We present advances to a system that accelerates Secret Sharing MPC using FPGA network cards, which is especially applicable for deployments where all parties are in the same data center. This approach makes improvements over previous work in this area. First, we replace TCP/IP with RDMA over FPGA SmartNICs; this sends data directly among memories without CPU involvement. And second, computation is overlapped with communication to avoid device idling.We implement the SmartNICs using AMD FPGAs with Coyote’s RDMA stack. The proof-of-concept application is a simple machine learning model. We performed layer-by-layer experiments to analyze latency. For the convolutional layers, the system achieves a 2.1× speedup compared to existing MPC frameworks. Moreover, based on these optimizations and analysis, we estimate that the machine learning workflow can achieve a 1.7× speedup compared to unoptimized MPC, potentially making MPC machine learning significantly more attractive.