# A Learning-Based Approach to Approximate Coded Computation

arXiv: https://arxiv.org/abs/2205.09818 · HTML: https://mlsanity.com/paper/2205.09818/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 11 of 11)

- [DRACO: Byzantine-resilient Distributed Training via Redundant Gradients](https://mlsanity.com/paper/1803.09877/) (2018)
- [Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy](https://mlsanity.com/paper/1806.00939/) (2018)
- [Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication](https://mlsanity.com/paper/1705.10464/) (2017)
- [On the Optimal Recovery Threshold of Coded Matrix Multiplication](https://mlsanity.com/paper/1801.10292/) (2018)
- [CodedSketch: A Coding Scheme for Distributed Computation of Approximated Matrix Multiplication](https://mlsanity.com/paper/1812.10460/) (2018)
- [Analog Lagrange Coded Computing](https://mlsanity.com/paper/2008.08565/) (2020)
- [OverSketch: Approximate Matrix Multiplication for the Cloud](https://mlsanity.com/paper/1811.02653/) (2018)
- [Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation](https://mlsanity.com/paper/1806.01259/) (2018)
- [Closed-form max-min power control for some cellular and cell-free massive MIMO networks](https://mlsanity.com/paper/2205.00429/) (2022)
- [Random Sampling for Distributed Coded Matrix Multiplication](https://mlsanity.com/paper/1905.06942/) (2019)
- [Successive Approximation Coding for Distributed Matrix Multiplication](https://mlsanity.com/paper/2201.03486/) (2022)

## Abstract (arXiv)

Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials. In this paper, we propose AICC, an AI-aided learning approach that is inspired by LCC but also uses deep neural networks (DNNs). It is appropriate for coded computation of more general functions. Numerical simulations demonstrate the suitability of the proposed approach for the coded computation of different matrix functions that are often utilized in digital signal processing.