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arXiv 2024年

作者： Heusdens, Richard Zhang, Guoqiang Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands University of Exeter Exeter United Kingdom

In this paper we consider the problem of distributed nonlinear optimisation of a separable convex cost function over a graph subject to cone constraints. We show how to generalise, using convex analysis, monotone operator theory and fixed-point theory, the primal-dual method of multipliers (PDMM), originally designed for equality constraint optimisation and recently extended to include linear inequality constraints, to accommodate for cone constraints. The resulting algorithm can be used to implement a variety of optimisation problems, including the important class of semidefinite programs with partially separable structure, in a fully distributed fashion. We derive update equations by applying the Peaceman-Rachford splitting algorithm to the monotonic inclusion related to the lifted dual problem. The cone constraints are implemented by a reflection method in the lifted dual domain where auxiliary variables are reflected with respect to the intersection of the polar cone and a subspace relating the dual and lifted dual domain. Convergence results for both synchronous and stochastic update schemes are provided and an application of the proposed algorithm is demonstrated to implement an approximate algorithm for maximum cut problems based on semidefinite programming in a fully distributed fashion. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Saei-DT: Semi-asynchronous Edge Intelligence for Industrial Digital Twin Networks in 6G

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IEEE Network 2024年

作者： Tao, Ming Liao, Lingling Xie, Renping Zhang, Yin Min, Geyong Zhang, Yan Dongguan University of Technology School of Computer Science and Technology China University of Electronic Science and Technology of China School of Information and Communication Engineering China University of Exeter Department of Computer Science United Kingdom University of Oslo School of Information Engineering Norway

Promoted by integrating sensing, communication, and computing for edge intelligence in 6G networks, the Digital Twin (DT), as a typical 6G application, has been widely employed for minimizing the disruption of Industrial Internet-of-Things (IIoT) operations and facilitating the cost-effective execution through conducting equivalent model training. Nevertheless, in complex and changeable industrial scenarios, the existing approaches of DT model construction and update within the Digital Twin Edge Network (DITEN), do not fully consider the heterogeneity in computing and communication capabilities, resulting in unbalanced model training cost and loss, which also cannot respond quickly to the environmental changes and ensure the data consistency of virtual-real space. To realize the collaborative and efficient utilization of heterogeneous computing and communication resources, and strengthen the balance of training cost and loss in the whole life cycle of DT model, an approach of using semi-asynchronous federated learning (FL) for DT construction and update, named Saei-DT, is proposed, which includes the three significant operations, e.g., terminal selection for model training, self-adaption of local training iterations, and semi-asynchronous global aggregation. Through conducting extensive experiments on the dataset of Industrial-Equipment contained in Kaggle, the experimental results demonstrate the efficiency of Saei-DT in both training accuracy and loss. © 2024 IEEE.

关键词： Federated learning

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A Novel Graph-Representation-Based Multi-Scale Feature Fusion Approach for Road Segmentation

A Novel Graph-Representation-Based Multi-Scale Feature Fusio...

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IEEE International Conference on Networking, Sensing and Control

作者： Zhenyang Li Siyang Song Fuxiang Chen Lu Liu School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Computer Science University of Exeter Exeter United Kingdom

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Exploiting contextual information is important for road segmentation tasks. In contrast to existing work primarily focusing on expanding the receptive field and stacking network layers, we propose a novel Graph-representation-based Multi-scale Feature Fusion U-net model (GMFFUnet) to address this problem. Our method enhances global context extraction by establishing connections between features of varying types and scales. Specifically, our model projects the entire feature pyramid into multiple interaction spaces and models the complex relationships among them to construct a graph and facilitate information exchange. The enhanced features are then fused back into the original feature pyramid. Our method provides substantial benefits on road segmentation tasks with complex backgrounds. Comparative experiments were carried out to evaluate the performance of the proposed method, and the results demonstrated that our proposed method outperforms all SOTA methods on the Massachusetts road dataset.

关键词： Adaptation models Roads Stacking Neural networks Focusing Feature extraction Solids Sensors Information exchange

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Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks

arXiv

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arXiv 2024年

作者： Yu, Lu Chang, Zheng Jia, Yunjian Min, Geyong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Microelectronics and Communication Engineering Chongqing University Chongqing China Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom

The integration of autonomous driving technologies with vehicular networks presents significant challenges in privacy preservation, communication efficiency, and resource allocation. This paper proposes a novel U-shaped split federated learning (U-SFL) framework to address these challenges on the way of realizing in vehicular edge networks. U-SFL is able to enhance privacy protection by keeping both raw data and labels on the vehicular user (VU) side while enabling parallel processing across multiple vehicles. To optimize communication efficiency, we introduce a semantic-aware auto-encoder (SAE) that significantly reduces the dimensionality of transmitted data while preserving essential semantic information. Furthermore, we develop a deep reinforcement learning (DRL) based algorithm to solve the NP-hard problem of dynamic resource allocation and split point selection. Our comprehensive evaluation demonstrates that U-SFL achieves comparable classification performance to traditional split learning (SL) while substantially reducing data transmission volume and communication latency. The proposed DRL-based optimization algorithm shows good convergence in balancing latency, energy consumption, and learning performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization

arXiv

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arXiv 2025年

作者： Ma, Youpeng Chen, Tao Li, Ke School of Computer Science and Engineering University of Electronic Science and Technology of China China IDEAS Lab School of Computer Science University of Birmingham United Kingdom Department of Computer Science University of Exeter United Kingdom

As software systems become more complex and configurable, more performance problems tend to arise from the configuration designs. This has caused some configuration options to unexpectedly degrade performance which deviates from their original expectations designed by the developers. Such discrepancies, namely configuration performance bugs (CPBugs), are devastating and can be deeply hidden in the source code. Yet, efficiently testing CPBugs is difficult, not only due to the test oracle is hard to set, but also because the configuration measurement is expensive and there are simply too many possible configurations to test. As such, existing testing tools suffer from lengthy runtime or have been ineffective in detecting CPBugs when the budget is limited, compounded by inaccurate test oracle. In this paper, we seek to achieve significantly faster CPBug testing by neurally prioritizing the testing at both the configuration option and value range levels with automated oracle estimation. Our proposed tool, dubbed NDP, is a general framework that works with different heuristic generators. The idea is to leverage two neural language models: one to estimate the CPBug types that serve as the oracle while, more vitally, the other to infer the probabilities of an option being CPBug-related, based on which the options and the value ranges to be searched can be prioritized. Experiments on several widely-used systems of different versions reveal that NDP can, in general, better predict CPBug type in 87% cases and find more CPBugs with up to 88.88× testing efficiency speedup over the state-of-the-art tools. Copyright © 2025, The Authors. All rights reserved.

关键词： Program debugging

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Privacy Respecting Design Principles for Smart Home Devices

SSRN

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SSRN 2023年

作者： Murphy, Rory Keedwell, Ed Hartley, Sarah Department of Computer Science University of Exeter Devon Exeter United Kingdom Department of Science Innovation Technology and Entrepreneurship University of Exeter Business School Devon Exeter United Kingdom

The Internet of Things (IoT) is growing and in the push to confer ‘smartness’ to new objects and environments, smart home devices (SHDs) are entering the historically private space of home. Within, their presence and function disrupt established socio informational norms, weakening privacy and creating data disempowerment. These disruptions are GDPR compliant, showing that regulatory data protections are insufficient to protect privacy in the home. In response, ethics and engineering must cohere to respect privacy in newly smart homes. However, there exist few contemporary SHD design principle papers and none using the lenses of respect and contextual integrity to fully address privacy. To embed privacy sensitivity in SHD research and innovation this paper develops: a privacy framework connecting respect and contextual integrity;a novel methodology for privacy respecting principle generation;and normatively informed principles offering specific design recommendations to respect privacy in the home context. © 2023, The Authors. All rights reserved.

关键词： Automation

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Privacy-Preserved Incentive Mechanism for Split Learning in Edge Computing System

Privacy-Preserved Incentive Mechanism for Split Learning in ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Lu Yu Zheng Chang Timo Hämäläinen Geyong Min School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Department of Computer Science University of Exeter Exeter U.K.

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In the edge computing system, split learning (SL) is an emerging distributed learning approach that allows mobile users (MUs) and edge nodes (ENs) to train the model together without sharing the raw data of the MU. Although MU can preserve its privacy in SL, attacks on the intermediate data at the cut layer for model training can still lead to privacy leakage, which prevents privacy-sensitive MUs from participating in training. Therefore, it is important to implement an effective incentive mechanism to motivate MUs to join SL while preserving their privacy. In this work, from the perspective of maximizing the utility of edge service provider (ESP) while considering the privacy-sensitivity of different MUs, the incentive problem of ESP and MUs is transformed into the utility optimization problem, and an incentive mechanism based on the differential privacy (DP) and contract theory is established to model the interactions between the MUs and EN. The obtained convex optimization problem is obtained through the mathematical derivations, and the optimal contract is presented by solving this problem. The simulation results demonstrate the effectiveness of our proposed privacy preserved incentive mechanism.

关键词： Training Privacy Computational modeling Simulation Distributed databases Data models Convex functions Contracts Optimization Edge computing

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ADDRESSING THE SUSTAINABLE AI TRILEMMA: A CASE STUDY ON LLM AGENTS AND RAG

arXiv

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arXiv 2025年

作者： Wu, Hui Wang, Xiaoyang Fan, Zhong Dept. of Engineering University of Exeter ExeterEX4 4QF United Kingdom Dept. of Computer Science University of Exeter ExeterEX4 4QF United Kingdom

Large language models (LLMs) have demonstrated significant capabilities, but their widespread deployment and more advanced applications raise critical sustainability challenges, particularly in inference energy consumption. We propose the concept of the Sustainable AI Trilemma, highlighting the tensions between AI capability, digital equity, and environmental sustainability. Through a systematic case study of LLM agents and retrieval-augmented generation (RAG), we analyze the energy costs embedded in memory module designs and introduce novel metrics to quantify the trade-offs between energy consumption and system performance. Our experimental results reveal significant energy inefficiencies in current memory-augmented frameworks and demonstrate that resource-constrained environments face disproportionate efficiency penalties. Our findings challenge the prevailing LLM-centric paradigm in agent design and provide practical insights for developing more sustainable AI systems. Our code is available online1 © 2025, CC BY.

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Empirical Studies of Resampling Strategies in Noisy Evolutionary Multi-Objective Optimization

Empirical Studies of Resampling Strategies in Noisy Evolutio...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shasha Zhou Ke Li College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science University of Exeter Exeter UK

Optimization problems are ubiquitous in real-world engineering scenarios where the goals are to enhance interested aspects such as efficiency, productivity, and profitability. However, solving practical optimization problems could be non-trivial, partly due to the presence of a wide range of noises, including environmental noises, model biases, time-domain variations, measurement uncertainties and many other uncontrolled variables. In this paper, we empirically study the effect of noise range, sample size and resampling type on the solution quality of MOEAs when noise is added to decision variables. Our empirical results, conducted on three commonly used Multi-Objective Optimization Problems (MOEAs), i.e. NSGA-II, MOEA/D and IBEA, demonstrate that noise range has more significant impact on the robustness of optimization algorithms compared to sample size and resampling type. In addition, we introduce the concept of bad point, which is able to illustrate how noise affects the performance of different MOEAs.

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Preference-Based Multi-Objective Optimization with Gaussian Process

Preference-Based Multi-Objective Optimization with Gaussian ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tian Huang Ke Li Department of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science University of Exeter Exeter UK

Traditional evolutionary multi-objective optimization (EMO) algorithm is to generate a set of non-dominated solutions on the Pareto front (PF). However, this technique falls short of delivering the outcomes for multi-objective optimization problems (MOPs) containing user preference. In this paper, we present a novel EMO algorithm that incorporates user preferences via a decision maker (DM). Our approach comprises three modules: consultation, preference elicitation and optimization. The DM undertakes the consultation and preference elicitation using Gaussian process (GP) to provide preference information. We employ the decomposition-based EMO algorithm (i.e., MOEA/D) for optimization. The experiment comprises two sessions. Firstly, we simulate the decision maker module with GP. Secondly, we simulate our proposed method and compare its performance with existing interactive optimization algorithms. Our research proposes a new preference-based EMO algorithm that addresses the shortcomings of traditional techniques and unlocks new possibilities for multi-objective optimization.

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