Collaborative Safe Bayesian Optimization

2026-02-18

AI Summary

This work addresses the challenge of efficiently tuning parameters in mobile networks under multidimensional safety constraints to adapt to dynamic traffic and channel variations. Existing approaches are either overly conservative or computationally expensive. To overcome these limitations, this study introduces safe Bayesian optimization to mobile network management for the first time and proposes a collaborative safe Bayesian optimization algorithm, CoSBO. CoSBO integrates multi-task learning with safety-constrained modeling to enable cross-task information sharing and facilitate efficient online parameter tuning. Experimental results demonstrate that, compared to SafeOpt-MC, CoSBO achieves significantly higher sample efficiency during the initial optimization phase and converges more rapidly to safe parameter configurations.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Mobile networks require safe optimization to adapt to changing conditions in traffic demand and signal transmission quality, in addition to improving service performance metrics. With the increasing complexity of emerging mobile networks, traditional parameter tuning methods become too conservative or complex to evaluate. For the first time, we apply safe Bayesian optimization to mobile networks. Moreover, we develop a new safe collaborative optimization algorithm called CoSBO, leveraging information from multiple optimization tasks in the network and considering multiple safety constraints. The resulting algorithm is capable of safely tuning the network parameter online with very few iterations. We demonstrate that the proposed method improves sample efficiency in the early stages of the optimization process by comparing it against the SafeOpt-MC algorithm in a mobile network scenario.

Problem

• safe optimization
• mobile networks
• parameter tuning
• safety constraints
• Bayesian optimization

Innovation

• Safe Bayesian Optimization
• Collaborative Optimization
• CoSBO
• Mobile Networks
• Sample Efficiency