# Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework

Source: [Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework](<https://www.sota2.com/research/paper/68fa5c881e209131ce9d1e44>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-27T05:00:04.193Z

Authors: Parsa Moradi, Behrooz Tahmasebi, Mohammad Ali Maddah-Ali

Published: 2024-06-01T05:01:25.000Z

## Abstract

Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromised servers. In this approach, each worker node processes a combination of the data, rather than the raw data itself. The final result then is decoded from the collective outputs of the worker nodes. However, there is a significant gap between current coded computing approaches and the broader landscape of general distributed computing, particularly when it comes to machine learning workloads. To bridge this gap, we propose a novel foundation for coded computing, integrating the principles of learning theory, and developing a framework that seamlessly adapts with machine learning applications. In this framework, the objective is to find the encoder and decoder functions that minimize the loss function, defined as the mean squared error between the estimated and true values. Facilitating the search for the optimum decoding and functions, we show that the loss function can be upper-bounded by the summation of two terms: the generalization error of the decoding function and the training error of the encoding function. Focusing on the second-order Sobolev space, we then derive the optimal encoder and decoder. We show that in the proposed solution, the mean squared error of the estimation decays with the rate of $\\mathcal{O}(S^3 N^{-3})$ and $\\mathcal{O}(S^{\\frac{8}{5}}N^{\\frac{-3}{5}})$ in noiseless and noisy computation settings, respectively, where $N$ is the number of worker nodes with at most $S$ slow servers (stragglers). Finally, we evaluate the proposed scheme on inference tasks for various machine learning models and demonstrate that the proposed framework outperforms the state-of-the-art in terms of accuracy and rate of convergence.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Inference on MNIST (RMSE, RelAcc)](<https://www.sota2.com/research/sota/inference-on-mnist-rmse-relacc.md>): Inference; MNIST; RMSE: 2.18.

- [Inference on CIFAR-10](<https://www.sota2.com/research/sota/inference-on-cifar-10.md>): Inference; CIFAR-10; RMSE: 2.04.

- [Inference on ImageNet 1K](<https://www.sota2.com/research/sota/inference-on-imagenet-1k.md>): Inference; ImageNet-1K; RMSE: 0.62.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.00300>)

[PDF](<https://arxiv.org/pdf/2406.00300.pdf>)