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

Elahe Vedadi, Yasaman Keshtkarjahromi, Hulya Seferoglu
2022 arXiv   pre-print
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
more » ... olynomial (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.
arXiv:2203.06759v2 fatcat:obp33girdvds7l2lotpqrhrxhe