用强化学习自动优化边缘设备的模型版本更新，提升安全与性能。
Effective ML Model Versioning in Edge Networks
- 用强化学习实现模型更新自动化，适应边缘环境约束。
- 在不同服务器负载下，能提升模型准确率、安全性和可靠性。
- 适合关注边缘AI部署稳定性的研发与运维人员。
机器学习模型、数据和软件需要在有重要版本更新且可集成时定期更新。这在边缘环境中尤为困难，因系统存在性能、响应时间、安全性和可靠性等多重约束，且更新可能严重影响鲁棒性与稳定性。本文首次提出机器学习模型版本优化问题，并提出有效解决方案，包括基于强化学习（RL）的更新自动化算法。研究聚焦于边缘网络环境，因其固有的性能、响应时间、安全与可靠性限制使更新更具挑战性。实验表明，通过强化学习方法可完全并有效地实现模型版本更新自动化。在各类服务器负载条件下，均可找到合适的版本策略，在保障较低响应时间的同时，提升安全性、可靠性和模型准确性。
原文摘要 · Abstract (English)
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.
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