# Privacy-Aware Joint DNN Model Deployment and Partitioning Optimization for Collaborative Edge Inference Services

arXiv: https://arxiv.org/abs/2502.16091 · HTML: https://mlsanity.com/paper/2502.16091/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 22 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1801.08618/) (2018)
- [Communication-Computation Trade-Off in Resource-Constrained Edge Inference](https://mlsanity.com/paper/2006.02166/) (2020)
- [A Survey on Collaborative DNN Inference for Edge Intelligence](https://mlsanity.com/paper/2207.07812/) (2022)
- [Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inference](https://mlsanity.com/paper/2105.13618/) (2021)
- [Collaborative Inference for AI-Empowered IoT Devices](https://mlsanity.com/paper/2207.11664/) (2022)
- [iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud](https://mlsanity.com/paper/2211.01713/) (2022)
- [TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networks](https://mlsanity.com/paper/2405.03990/) (2024)
- [Joint Foundation Model Caching and Inference of Generative AI Services for Edge Intelligence](https://mlsanity.com/paper/2305.12130/) (2023)
- [Guardians of the Deep Fog: Failure-Resilient DNN Inference from Edge to Cloud](https://mlsanity.com/paper/1909.00995/) (2019)
- [DistPrivacy: Privacy-Aware Distributed Deep Neural Networks in IoT surveillance systems](https://mlsanity.com/paper/2010.13234/) (2020)
- [RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems](https://mlsanity.com/paper/2208.13032/) (2022)
- [Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing](https://mlsanity.com/paper/2310.13384/) (2023)
- [GainNet: Coordinates the Odd Couple of Generative AI and 6G Networks](https://mlsanity.com/paper/2401.02662/) (2024)

## Built on by (most cited first, 2 of 2)

- [Onboard Optimization and Learning: A Survey](https://mlsanity.com/paper/2505.08793/) (2025)
- [Safe Multi-Agent Deep Reinforcement Learning for Privacy-Aware Edge-Device Collaborative DNN Inference](https://mlsanity.com/paper/2603.00129/) (2026)

## Abstract (arXiv)

Edge inference (EI) has emerged as a promising paradigm to address the growing limitations of cloud-based Deep Neural Network (DNN) inference services, such as high response latency, limited scalability, and severe data privacy exposure. However, deploying DNN models on resource-constrained edge devices introduces additional challenges, including limited computation/storage resources, dynamic service demands, and heightened privacy risks. To tackle these issues, this paper presents a novel privacy-aware optimization framework that jointly addresses DNN model deployment, user-server association, and model partitioning, with the goal of minimizing long-term average inference delay under resource and privacy constraints. The problem is formulated as a complex, NP-hard stochastic optimization. To efficiently handle system dynamics and computational complexity, we employ a Lyapunov-based approach to transform the long-term objective into tractable per-slot decisions. Furthermore, we introduce a coalition formation game to enable adaptive user-server association and design a greedy algorithm for model deployment within each coalition. Extensive simulations demonstrate that the proposed algorithm significantly reduces inference delay and consistently satisfies privacy constraints, outperforming state-of-the-art baselines across diverse scenarios.