# Effective ML Model Versioning in Edge Networks

arXiv: https://arxiv.org/abs/2411.01078 · HTML: https://mlsanity.com/paper/2411.01078/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 6 of 6)

- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates](https://mlsanity.com/paper/2402.17390/) (2024)
- [Scaling Serverless Functions in Edge Networks: A Reinforcement Learning Approach](https://mlsanity.com/paper/2305.13130/) (2023)
- [Modeling of Deep Neural Network (DNN) Placement and Inference in Edge Computing](https://mlsanity.com/paper/2001.06901/) (2020)
- [Towards Optimal Serverless Function Scaling in Edge Computing Network](https://mlsanity.com/paper/2305.13896/) (2023)

## Abstract (arXiv)

Machine learning (ML) models, data and software need to be regularly updated whenever essential version updates are released and feasible for integration. This is a basic but most challenging requirement to satisfy in the edge, due to the various system constraints and the major impact that an update can have on robustness and stability. In this paper, we formulate for the first time the ML model versioning optimization problem, and propose effective solutions, including the update automation with reinforcement learning (RL) based algorithm. We study the edge network environment due to the known constraints in performance, response time, security, and reliability, which make updates especially challenging. The performance study shows that model version updates can be fully and effectively automated with reinforcement learning method. We show that for every range of server load values, the proper versioning can be found that improves security, reliability and/or ML model accuracy, while assuring a comparably lower response time.