  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.
