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Multi-party computation (MPC) is promising for designing privacy-preservingmachine learning algorithms at edge networks. An emerging approach is coded-MPC(CMPC), which advocates the use of coded computation to improve the performanceof MPC in terms of the required number of workers involved in computations. Thecurrent approach for designing CMPC algorithms is to merely combine efficientcoded computation constructions with MPC. Instead, we propose a newconstruction; Adaptive Gap Entangled polynomial (AGE) codes, where the degreesof polynomials used in computations are optimized for MPC. We show that MPCwith AGE codes (AGE-CMPC) performs better than existing CMPC algorithms interms of the required number of workers as well as storage, communication andcomputation load.

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