# Bayes-Split-Edge: Bayesian Optimization for Constrained Collaborative Inference in Wireless Edge Systems

arXiv: https://arxiv.org/abs/2510.23503 · HTML: https://mlsanity.com/paper/2510.23503/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 52 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1801.08618/) (2018)
- [Split Learning in 6G Edge Networks](https://mlsanity.com/paper/2306.12194/) (2023)
- [AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks](https://mlsanity.com/paper/2403.13101/) (2024)
- [Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation](https://mlsanity.com/paper/1610.07379/) (2016)
- [Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces](https://mlsanity.com/paper/2307.00618/) (2023)
- [Surrogate modeling for Bayesian optimization beyond a single Gaussian process](https://mlsanity.com/paper/2205.14090/) (2022)
- [Optimizing Coverage and Capacity in Cellular Networks using Machine Learning](https://mlsanity.com/paper/2010.13710/) (2020)
- [Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency](https://mlsanity.com/paper/2311.05739/) (2023)
- [Packet-Loss-Tolerant Split Inference for Delay-Sensitive Deep Learning in Lossy Wireless Networks](https://mlsanity.com/paper/2104.13629/) (2021)
- [Over-the-Air Split Machine Learning in Wireless MIMO Networks](https://mlsanity.com/paper/2210.04742/) (2022)
- [Adaptive Split Learning over Energy-Constrained Wireless Edge Networks](https://mlsanity.com/paper/2403.05158/) (2024)
- [pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data](https://mlsanity.com/paper/2501.09822/) (2025)
- [Neural-Enhanced Rate Adaptation and Computation Distribution for Emerging mmWave Multi-User 3D Video Streaming Systems](https://mlsanity.com/paper/2505.13337/) (2025)
- [Semantic-Aware and Goal-Oriented Communications for Object Detection in Wireless End-to-End Image Transmission](https://mlsanity.com/paper/2402.01064/) (2024)

## Built on by (most cited first, 1 of 1)

- [Adaptive Edge-Cloud Inference for Speech-to-Action Systems Using ASR and Large Language Models](https://mlsanity.com/paper/2512.12769/) (2025)

## Abstract (arXiv)

Mobile edge devices (e.g., AR/VR headsets) typically need to complete timely inference tasks while operating with limited on-board computing and energy resources. In this paper, we investigate the problem of collaborative inference in wireless edge networks, where energy-constrained edge devices aim to complete inference tasks within given deadlines. These tasks are carried out using neural networks, and the edge device seeks to optimize inference performance under energy and delay constraints. The inference process can be split between the edge device and an edge server, thereby achieving collaborative inference over wireless networks. We formulate an inference utility optimization problem subject to energy and delay constraints, and propose a novel solution called Bayes-Split-Edge, which leverages Bayesian optimization for collaborative split inference over wireless edge networks. Our solution jointly optimizes the transmission power and the neural network split point. The Bayes-Split-Edge framework incorporates a novel hybrid acquisition function that balances inference task utility, sample efficiency, and constraint violation penalties. We evaluate our approach using the VGG19 model on the ImageNet-Mini dataset, and Resnet101 on Tiny-ImageNet, and real-world mMobile wireless channel datasets. Numerical results demonstrate that Bayes-Split-Edge achieves up to 2.4x reduction in evaluation cost compared to standard Bayesian optimization and achieves near-linear convergence. It also outperforms several baselines, including CMA-ES, DIRECT, exhaustive search, and Proximal Policy Optimization (PPO), while matching exhaustive search performance under tight constraints. These results confirm that the proposed framework provides a sample-efficient solution requiring maximum 20 function evaluations and constraint-aware optimization for wireless split inference in edge computing systems.