Adaptive Gap Entangled Polynomial Coding for Multi-Party Computation at the Edge

TL;DR

This paper introduces Adaptive Gap Entangled polynomial (AGE) codes, optimizing polynomial degrees in coded-MPC to enhance privacy-preserving machine learning at the edge by reducing resource requirements.

Contribution

The paper proposes AGE codes, a novel construction that improves coded-MPC performance by optimizing polynomial degrees, outperforming existing methods in efficiency and resource usage.

Findings

AGE-CMPC reduces the number of workers needed.

AGE codes lower storage, communication, and computation loads.

Performance improvements over existing CMPC algorithms.

Abstract

Multi-party computation (MPC) is promising for designing privacy-preserving machine learning algorithms at edge networks. An emerging approach is coded-MPC (CMPC), which advocates the use of coded computation to improve the performance of MPC in terms of the required number of workers involved in computations. The current approach for designing CMPC algorithms is to merely combine efficient coded computation constructions with MPC. Instead, we propose a new construction; Adaptive Gap Entangled polynomial (AGE) codes, where the degrees of polynomials used in computations are optimized for MPC. We show that MPC with AGE codes (AGE-CMPC) performs better than existing CMPC algorithms in terms of the required number of workers as well as storage, communication and computation load.