# On Model Coding for Distributed Inference and Transmission in Mobile Edge Computing Systems

arXiv: https://arxiv.org/abs/1904.05591 · HTML: https://mlsanity.com/paper/1904.05591/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2112.08909/) (2021)
- [Joint Coding and Scheduling Optimization for Distributed Learning over Wireless Edge Networks](https://mlsanity.com/paper/2103.04303/) (2021)
- [Coding for Straggler Mitigation in Federated Learning](https://mlsanity.com/paper/2109.15226/) (2021)

## Abstract (arXiv)

Consider a mobile edge computing system in which users wish to obtain the result of a linear inference operation on locally measured input data. Unlike the offloaded input data, the model weight matrix is distributed across wireless Edge Nodes (ENs). ENs have non-deterministic computing times, and they can transmit any shared computed output back to the users cooperatively. This letter investigates the potential advantages obtained by coding model information prior to ENs' storage. Through an information-theoretic analysis, it is concluded that, while generally limiting cooperation opportunities, coding is instrumental in reducing the overall computation-plus-communication latency.