# Machine Learning for Networking: Workflow, Advances and Opportunities

arXiv: https://arxiv.org/abs/1709.08339 · HTML: https://mlsanity.com/paper/1709.08339/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2017.

## Built on by (most cited first, 20 of 20)

- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://mlsanity.com/paper/2104.09027/) (2021)
- [RouteNet-Fermi: Network Modeling with Graph Neural Networks](https://mlsanity.com/paper/2212.12070/) (2022)
- [RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation](https://mlsanity.com/paper/2202.13956/) (2022)
- [Can LLMs Understand Computer Networks? Towards a Virtual System Administrator](https://mlsanity.com/paper/2404.12689/) (2024)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering](https://mlsanity.com/paper/2303.18157/) (2023)
- [Is Machine Learning Ready for Traffic Engineering Optimization?](https://mlsanity.com/paper/2109.01445/) (2021)
- [The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks](https://mlsanity.com/paper/2107.12433/) (2021)
- [Scaling Graph-based Deep Learning models to larger networks](https://mlsanity.com/paper/2110.01261/) (2021)
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://mlsanity.com/paper/2208.06963/) (2022)
- [A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario](https://mlsanity.com/paper/2202.01949/) (2022)
- [Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2211.12201/) (2022)
- [Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting](https://mlsanity.com/paper/2412.04081/) (2024)
- [Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks](https://mlsanity.com/paper/2302.11268/) (2023)
- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)
- [Ordered Topological Deep Learning: a Network Modeling Case Study](https://mlsanity.com/paper/2503.16746/) (2025)
- [Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle](https://mlsanity.com/paper/2404.18736/) (2024)
- [A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks](https://mlsanity.com/paper/2402.04515/) (2024)
- [Automatic Generation of Digital Twins for Network Testing](https://mlsanity.com/paper/2510.03205/) (2025)
- [Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study](https://mlsanity.com/paper/2609.18704/) (2026)

## Abstract (arXiv)

Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the net-working and distributed computing system is the key infrastructure to provide efficient computational resource for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of Machine Learning techniques for Networking (MLN), which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply the machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations on their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities on networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help and motivate researchers to develop innovative algorithms, standards and frameworks.