# Joint Coding and Scheduling Optimization for Distributed Learning over Wireless Edge Networks

arXiv: https://arxiv.org/abs/2103.04303 · HTML: https://mlsanity.com/paper/2103.04303/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 7 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/1907.08349/) (2019)
- [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020)
- [Speeding Up Distributed Machine Learning Using Codes](https://mlsanity.com/paper/1512.02673/) (2015)
- [Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2011.06223/) (2020)
- [Coded Computation over Heterogeneous Clusters](https://mlsanity.com/paper/1701.05973/) (2017)
- [Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning](https://mlsanity.com/paper/1803.05397/) (2018)
- ["Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications](https://mlsanity.com/paper/1904.03897/) (2019)
- [Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks](https://mlsanity.com/paper/1902.09696/) (2019)
- [On Model Coding for Distributed Inference and Transmission in Mobile Edge Computing Systems](https://mlsanity.com/paper/1904.05591/) (2019)
- [Hierarchical Coding for Distributed Computing](https://mlsanity.com/paper/1801.04686/) (2018)
- [Distributed Deep Learning at the Edge: A Novel Proactive and Cooperative Caching Framework for Mobile Edge Networks](https://mlsanity.com/paper/1812.05374/) (2018)
- [Delay-Optimal Distributed Edge Computing in Wireless Edge Networks](https://mlsanity.com/paper/2002.02596/) (2020)
- [DeepFake: Deep Dueling-based Deception Strategy to Defeat Reactive Jammers](https://mlsanity.com/paper/2005.07034/) (2020)

## Built on by (most cited first, 5 of 5)

- [Machine Unlearning: A Survey](https://mlsanity.com/paper/2306.03558/) (2023)
- [Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2404.13804/) (2024)
- [A Survey on AI for 6G: Challenges and Opportunities](https://mlsanity.com/paper/2604.02370/) (2026)
- [Coded Computation across Shared Heterogeneous Workers with Communication Delay](https://mlsanity.com/paper/2109.11246/) (2021)
- [Design and Optimization of Hierarchical Gradient Coding for Distributed Learning at Edge Devices](https://mlsanity.com/paper/2406.10831/) (2024)

## Abstract (arXiv)

Unlike theoretical distributed learning (DL), DL over wireless edge networks faces the inherent dynamics/uncertainty of wireless connections and edge nodes, making DL less efficient or even inapplicable under the highly dynamic wireless edge networks (e.g., using mmW interfaces). This article addresses these problems by leveraging recent advances in coded computing and the deep dueling neural network architecture. By introducing coded structures/redundancy, a distributed learning task can be completed without waiting for straggling nodes. Unlike conventional coded computing that only optimizes the code structure, coded distributed learning over the wireless edge also requires to optimize the selection/scheduling of wireless edge nodes with heterogeneous connections, computing capability, and straggling effects. However, even neglecting the aforementioned dynamics/uncertainty, the resulting joint optimization of coding and scheduling to minimize the distributed learning time turns out to be NP-hard. To tackle this and to account for the dynamics and uncertainty of wireless connections and edge nodes, we reformulate the problem as a Markov Decision Process and then design a novel deep reinforcement learning algorithm that employs the deep dueling neural network architecture to find the jointly optimal coding scheme and the best set of edge nodes for different learning tasks without explicit information about the wireless environment and edge nodes' straggling parameters. Simulations show that the proposed framework reduces the average learning delay in wireless edge computing up to 66% compared with other DL approaches. The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.