# A Survey of Coded Distributed Computing

arXiv: https://arxiv.org/abs/2008.09048 · HTML: https://mlsanity.com/paper/2008.09048/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning](https://mlsanity.com/paper/2103.01206/) (2021)
- [A Survey of Secure Computation Using Trusted Execution Environments](https://mlsanity.com/paper/2302.12150/) (2023)
- [Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives](https://mlsanity.com/paper/2407.11280/) (2024)
- [Coded Computation across Shared Heterogeneous Workers with Communication Delay](https://mlsanity.com/paper/2109.11246/) (2021)
- [Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning](https://mlsanity.com/paper/2206.02450/) (2022)
- [Privacy-aware Berrut Approximated Coded Computing for Federated Learning](https://mlsanity.com/paper/2405.01704/) (2024)
- [Privacy-aware Berrut Approximated Coded Computing applied to general distributed learning](https://mlsanity.com/paper/2505.06759/) (2025)
- [Successive Refinement in Large-Scale Computation: Advancing Model Inference Applications](https://mlsanity.com/paper/2402.07229/) (2024)

## Abstract (arXiv)

Distributed computing has become a common approach for large-scale computation of tasks due to benefits such as high reliability, scalability, computation speed, and costeffectiveness. However, distributed computing faces critical issues related to communication load and straggler effects. In particular, computing nodes need to exchange intermediate results with each other in order to calculate the final result, and this significantly increases communication overheads. Furthermore, a distributed computing network may include straggling nodes that run intermittently slower. This results in a longer overall time needed to execute the computation tasks, thereby limiting the performance of distributed computing. To address these issues, coded distributed computing (CDC), i.e., a combination of coding theoretic techniques and distributed computing, has been recently proposed as a promising solution. Coding theoretic techniques have proved effective in WiFi and cellular systems to deal with channel noise. Therefore, CDC may significantly reduce communication load, alleviate the effects of stragglers, provide fault-tolerance, privacy and security. In this survey, we first introduce the fundamentals of CDC, followed by basic CDC schemes. Then, we review and analyze a number of CDC approaches proposed to reduce the communication costs, mitigate the straggler effects, and guarantee privacy and security. Furthermore, we present and discuss applications of CDC in modern computer networks. Finally, we highlight important challenges and promising research directions related to CDC