# A Unified Coding Framework for Distributed Computing with Straggling Servers

arXiv: https://arxiv.org/abs/1609.01690 · HTML: https://mlsanity.com/paper/1609.01690/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2016.

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

- [CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2112.08909/) (2021)
- [List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier](https://mlsanity.com/paper/2101.11653/) (2021)
- [Coding for Straggler Mitigation in Federated Learning](https://mlsanity.com/paper/2109.15226/) (2021)
- [Coded-InvNet for Resilient Prediction Serving Systems](https://mlsanity.com/paper/2106.06445/) (2021)
- [Coded Computation across Shared Heterogeneous Workers with Communication Delay](https://mlsanity.com/paper/2109.11246/) (2021)
- [FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning](https://mlsanity.com/paper/2509.07342/) (2025)
- [Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation](https://mlsanity.com/paper/2204.11168/) (2022)
- [Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates](https://mlsanity.com/paper/2205.01470/) (2022)

## Abstract (arXiv)

We propose a unified coded framework for distributed computing with straggling servers, by introducing a tradeoff between "latency of computation" and "load of communication" for some linear computation tasks. We show that the coded scheme of [1]-[3] that repeats the intermediate computations to create coded multicasting opportunities to reduce communication load, and the coded scheme of [4], [5] that generates redundant intermediate computations to combat against straggling servers can be viewed as special instances of the proposed framework, by considering two extremes of this tradeoff: minimizing either the load of communication or the latency of computation individually. Furthermore, the latency-load tradeoff achieved by the proposed coded framework allows to systematically operate at any point on that tradeoff to perform distributed computing tasks. We also prove an information-theoretic lower bound on the latency-load tradeoff, which is shown to be within a constant multiplicative gap from the achieved tradeoff at the two end points.