Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

• URL: http://arxiv.org/abs/2502.16091v2
• Date: Sat, 01 Mar 2025 04:35:04 GMT
• Title: Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference
• Authors: Zhipeng Cheng, Xiaoyu Xia, Hong Wang, Minghui Liwang, Ning Chen, Xuwei Fan, Xianbin Wang,
• Abstract summary: Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference.<n>This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints.
• Score: 14.408050197587654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios.

Related papers

• Robust DNN Partitioning and Resource Allocation Under Uncertain Inference Time [12.359690205873335]
  In edge intelligence systems, deep neural network (DNN) partitioning and data offloading can provide real-time task inference for resource-constrained mobile devices. We propose a robust optimization scheme to minimize the total energy consumption of mobile devices while meeting task probabilistic deadlines.
  arXiv  Detail & Related papers  (2025-03-27T13:06:26Z)
• Resource-Efficient Generative AI Model Deployment in Mobile Edge Networks [15.958822667638405]
  The scarcity of available resources on the edge pose significant challenges in deploying generative AI models. We present a collaborative edge-cloud framework aiming to properly manage generative AI model deployment on the edge.
  arXiv  Detail & Related papers  (2024-09-09T03:17:28Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework [14.518558523319518]
  Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard wireless resource allocation problems. We propose a novel unsupervised learning framework to solve the classical power control problem in a multi-user channel. We show that the proposed solutions not only improve the data rate but also achieve zero constraint violation probability, compared to the existing computations.
  arXiv  Detail & Related papers  (2023-05-31T14:11:51Z)
• Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning [11.007816552466952]
  This paper focuses on the problem of scheduling inference queries on Deep Neural Networks in edge networks at short timescales. By means of simulations, we analyze several policies in the realistic network settings and workloads of a large ISP. We design ASET, a Reinforcement Learning based scheduling algorithm able to adapt its decisions according to the system conditions.
  arXiv  Detail & Related papers  (2023-01-31T13:23:34Z)
• Architecture Aware Latency Constrained Sparse Neural Networks [35.50683537052815]
  In this paper, we design an architecture aware latency constrained sparse framework to prune and accelerate CNN models. We also propose a novel sparse convolution algorithm for efficient computation. Our system-algorithm co-design framework can achieve much better frontier among network accuracy and latency on resource-constrained mobile devices.
  arXiv  Detail & Related papers  (2021-09-01T03:41:31Z)
• Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks [94.85780721466816]
  A novel framework for proactive caching is proposed in this paper. It combines model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost.
  arXiv  Detail & Related papers  (2021-08-15T21:32:47Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)
• Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels [69.18343801164741]
  partitioned edge learning (PARTEL) implements parameter-server training, a well known distributed learning method, in wireless network. We consider the case of deep neural network (DNN) models which can be trained using PARTEL by introducing some auxiliary variables.
  arXiv  Detail & Related papers  (2020-10-08T15:27:50Z)
• Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence [21.55340197267767]
  Mobile Edge Computing (MEC) has emerged as a promising supporting architecture providing a variety of resources to the network edge. With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN) based AI applications. We propose an algorithm called Iterative Alternating Optimization (IAO) that can achieve the optimal solution in time.
  arXiv  Detail & Related papers  (2020-07-15T09:40:13Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.