mlsanityData to 14 Oct 2026

Effective ML Model Versioning in Edge Networks

arXiv 2411.01078 ↗First posted 1 Nov 2024cs.NI cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

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.

Builds on

6 of 11 references.

References · 6

  1. 2020Challenges in Deploying Machine Learning: a Survey of Case Studiescited by 152
  2. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 128
  3. 2024Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updatescited by 4
  4. 2023Scaling Serverless Functions in Edge Networks: A Reinforcement Learning Approachcited by 2
  5. 2020Modeling of Deep Neural Network (DNN) Placement and Inference in Edge Computingcited by 1
  6. 2023Towards Optimal Serverless Function Scaling in Edge Computing Networkcited by 1

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  1. 2025Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments
  2. 2025REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
  3. 2025Adaptive Network Security Policies via Belief Aggregation and Rollout
  4. 2024Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Management at Open RAN Edges
  5. 2020Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
  6. 2025Stabilizing Reinforcement Learning with LLMs: Formulation and Practices
  7. 2026Safe Multi-Agent Deep Reinforcement Learning for Privacy-Aware Edge-Device Collaborative DNN Inference
  8. 2024MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL