Computer Standards & Interfaces最新文献_第4页

A requirement-driven method for process mining based on model-driven engineering 基于模型驱动工程的需求驱动过程挖掘方法

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-04 DOI: 10.1016/j.csi.2025.104108

Selsabil Ines Bouhidel , Mohammed Mounir Bouhamed , Gregorio Diaz , Nabil Belala

Process mining analyzes business processes using event logs. Existing tools generate models to facilitate this task and improve the original business process, but the results are often unsatisfactory due to the complexity of the obtained models. Among the challenges faced in this context, we identify the misalignment with specific business requirements, preventing managers from accessing key data and making effective decisions. In this paper, we propose a requirement-driven approach centered on meta-modeling, which can help the development of process mining tools specially tailored to organizational needs. Thus, we introduce a requirement-driven method to address the critical challenge of model misalignment with required information. The method employs Model-Driven Engineering to simplify how process mining results are formulated, analyzed, and interpreted. The proposed method is iterative and involves several steps. First, a service manager defines a specific business question. Second, service managers and developers collaboratively establish a meta-model representing the target data. Third, developers extract relevant data using appropriate analysis techniques and visualize it. Finally, service managers and developers jointly interpret these visualizations to inform strategic decisions. This requirement-driven methodology empowers developers to concentrate on relevant information. Unlike general-purpose frameworks (e.g., ProM, Disco), this method emphasizes specificity, iterative refinement, and close stakeholder collaboration. By reducing cognitive overload through focused modeling and filtering of irrelevant data, organizations adopting this approach can achieve faster response times to business questions and develop specialized in-house analytical tools. This requirement-driven methodology, therefore, improves decision-making capabilities within process mining and across related analytical domains. We illustrate our methodology through a real business process taken from the literature owned by the VOLVO group. We use several examples of process mining to illustrate the benefits of the proposed methodology compared to existing tools which are unable to provide the required information.

流程挖掘使用事件日志分析业务流程。现有的工具生成模型来促进此任务并改进原始业务流程，但是由于所获得的模型的复杂性，结果往往不令人满意。在此上下文中面临的挑战中，我们确定了与特定业务需求的不一致，从而阻止了管理人员访问关键数据并做出有效决策。在本文中，我们提出了一种以元建模为中心的需求驱动方法，它可以帮助开发专门针对组织需求的过程挖掘工具。因此，我们引入了一种需求驱动的方法来解决模型与所需信息不一致的关键挑战。该方法采用模型驱动工程来简化过程挖掘结果的表述、分析和解释。所提出的方法是迭代的，涉及几个步骤。首先，服务管理器定义一个特定的业务问题。其次，服务管理人员和开发人员协作建立表示目标数据的元模型。第三，开发人员使用适当的分析技术提取相关数据并将其可视化。最后，服务经理和开发人员共同解释这些可视化，以告知战略决策。这种需求驱动的方法使开发人员能够专注于相关信息。与通用框架（例如，ProM、Disco）不同，该方法强调专一性、迭代细化和密切涉众协作。通过集中建模和过滤无关数据来减少认知超载，采用这种方法的组织可以实现对业务问题更快的响应时间，并开发专门的内部分析工具。因此，这种需求驱动的方法提高了过程挖掘和跨相关分析领域的决策能力。我们通过从沃尔沃集团拥有的文献中提取的真实业务流程来说明我们的方法。我们使用几个过程挖掘的例子来说明所提出的方法与现有工具相比的好处，这些工具无法提供所需的信息。

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引用次数: 0

MExpm: Fair computation offloading for batch modular exponentiation with improved privacy and checkability in IoV MExpm: IoV中批量模块化幂运算的公平计算卸载，改进了隐私性和可检查性

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-03 DOI: 10.1016/j.csi.2025.104107

Sipeng Shen , Qiang Wang , Fucai Zhou, Jian Xu, Mingxing Jin

Modular exponentiation is a fundamental cryptographic operation extensively applied in the Internet of Vehicles (IoV). However, its computational intensity imposes significant resource and time demands on intelligent vehicles. Offloading such computations to Mobile Edge Computing (MEC) servers has emerged as a promising approach. Nonetheless, existing schemes are generally impractical, as they either fail to ensure fairness between intelligent vehicles and MEC servers, lack privacy protection for the bases and exponents, or cannot guarantee the correctness of results with overwhelming probability due to potential misbehavior by MEC servers. To address these limitations, we propose MExpm, a fair and efficient computation offloading scheme for batch modular exponentiation under a single untrusted server model. Our scheme leverages blockchain technology to ensure fairness through publicly verifiable results. Furthermore, MExpm achieves high checkability, offering a near-perfect probability of checkability. To enhance privacy, we introduce secure obfuscation and logical split techniques, effectively protecting both the bases and the exponents. Extensive theoretical analysis and experimental results demonstrate that our scheme is not only efficient in terms of computation, communication, and storage overheads but also significantly improves privacy protection and checkability.

模幂运算是一种广泛应用于车联网（IoV）的基本加密运算。然而，其计算强度给智能汽车带来了巨大的资源和时间需求。将此类计算卸载到移动边缘计算（MEC）服务器已成为一种有前途的方法。然而，现有的方案要么无法保证智能车辆与MEC服务器之间的公平性，要么缺乏对基数和指数的隐私保护，要么由于MEC服务器可能存在的不当行为，无法保证结果的绝大多数概率的正确性，这些都是不切实际的。为了解决这些限制，我们提出了MExpm，这是一个公平有效的计算卸载方案，用于在单个不受信任的服务器模型下进行批量模块化幂运算。我们的方案利用区块链技术，通过可公开验证的结果来确保公平性。此外，MExpm实现了高可检查性，提供了近乎完美的可检查性概率。为了增强隐私性，我们引入了安全混淆和逻辑分割技术，有效地保护了基数和指数。大量的理论分析和实验结果表明，我们的方案不仅在计算、通信和存储开销方面有效，而且显著提高了隐私保护和可检查性。

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引用次数: 0

DEA-GAO: A two-stage approach optimal controller placement in software-defined networks using data envelopment analysis DEA-GAO：一种采用数据包络分析的两阶段方法在软件定义网络中优化控制器配置

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-02 DOI: 10.1016/j.csi.2025.104109

Fatemeh Dashti, Ali Ghaffari, Nahideh Derakhshanfard, Shiva Taghipoureivazi

Software-defined networks (SDN) have demonstrated considerable benefits in various practical domains by decoupling the control plane from the data plane, thus facilitating programmable network management. This paper presents a two-stage approach for solving the problem of controller placement called DEA-GAO. In the first stage, this strategy assumes the SDN network as a graph and using Data Envelopment Analysis (DEA) and relying on graph centrality metrics such as closeness centrality, betweenness centrality, and eigenvector centrality, calculates the efficiency of nodes to determine the optimal locations for deploying controllers. In the second stage, to allocate switches to controllers, the proposed strategy employs the Green Anaconda Optimization algorithm (GAO) to achieve an optimal allocation while considering network parameters such as average delay, load balancing, and reliability. Finally, to assess the efficacy of the proposed methodology, it is juxtaposed with three extant methods utilizing diverse datasets from the Internet Topology Zoo. The experimental findings indicate that the proposed approach significantly surpasses the existing methods, specifically the hybrid RDMCP-PSO algorithm, heuristic CPP algorithm and PSO algorithm in terms of both average delay (8.8 %, 28.8 % and 22.2 % respectively) and controller utilization (1.5 %, 7.3 % and 32 % respectively).

软件定义网络（SDN）通过将控制平面与数据平面解耦，从而促进可编程网络管理，在各种实际领域中显示出相当大的优势。本文提出了一种两阶段的方法来解决被称为DEA-GAO的控制器放置问题。在第一阶段，该策略将SDN网络假设为一个图，并使用数据包络分析（DEA），依靠图中心性指标（如接近中心性、中间中心性和特征向量中心性）计算节点的效率，以确定部署控制器的最佳位置。第二阶段，在考虑平均时延、负载均衡、可靠性等网络参数的情况下，采用绿色蟒蛇优化算法（Green Anaconda Optimization algorithm， GAO）实现交换机到控制器的最优分配。最后，为了评估所提出方法的有效性，将其与利用互联网拓扑动物园不同数据集的三种现有方法并置。实验结果表明，该方法在平均时延（分别为8.8%、28.8%和22.2%）和控制器利用率（分别为1.5%、7.3%和32%）方面均明显优于现有方法，特别是混合RDMCP-PSO算法、启发式CPP算法和PSO算法。

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引用次数: 0

Integrating IoT security practices into a risk-based framework for small and medium enterprises (SMEs) 将物联网安全实践整合到中小型企业（SMEs）的基于风险的框架中

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-11-26 DOI: 10.1016/j.csi.2025.104099

Samer Aoudi, Hussain Al-Aqrabi

The growing integration of Internet of Things (IoT) technologies within Small and Medium Enterprises (SMEs) has introduced new operational efficiencies while simultaneously expanding the cybersecurity threat landscape. However, most SMEs lack the resources, technical expertise, and institutional maturity required to adopt existing security frameworks, which are often designed with large enterprises in mind. This paper proposes a risk-based framework specifically developed to help SMEs identify, assess, and mitigate IoT-related security risks in a structured and scalable manner. The framework integrates key components such as asset classification, STRIDE-based threat modeling, CVSS-driven vulnerability assessment, and dynamic risk prioritization through Bayesian inference. Emphasis is placed on cost-effective mitigation strategies that are feasible within SME resource constraints and aligned with regulatory requirements. The framework was validated through a real-world case study involving a digitally enabled retail SME. Results demonstrate tangible improvements in vulnerability management, security control implementation, and organizational readiness. Additionally, qualitative feedback from stakeholders highlights the framework’s usability, adaptability, and minimal disruption to operations. This research bridges a critical gap in the current literature by contextualizing established cybersecurity methodologies for the SME sector and providing a practical toolset for managing IoT risks. The proposed framework offers SMEs a viable path toward improving cybersecurity resilience in increasingly connected business environments.

物联网（IoT）技术在中小企业（SMEs）中的日益融合，在带来新的运营效率的同时，也扩大了网络安全威胁的范围。然而，大多数中小企业缺乏采用现有安全框架所需的资源、技术专长和制度成熟度，这些框架通常是为大型企业设计的。本文提出了一个基于风险的框架，专门用于帮助中小企业以结构化和可扩展的方式识别、评估和减轻物联网相关的安全风险。该框架集成了资产分类、基于stride的威胁建模、cvss驱动的漏洞评估以及通过贝叶斯推理进行的动态风险优先级排序等关键组件。重点是在中小企业资源限制范围内可行并符合监管要求的具有成本效益的缓解战略。该框架通过涉及数字化零售中小企业的实际案例研究进行了验证。结果显示了在漏洞管理、安全控制实现和组织准备方面的切实改进。此外，来自涉众的定性反馈强调了框架的可用性、适应性和对操作的最小干扰。本研究通过将中小企业部门的既定网络安全方法置于背景中，并提供管理物联网风险的实用工具集，弥合了当前文献中的关键空白。拟议的框架为中小企业在日益互联的商业环境中提高网络安全弹性提供了一条可行的途径。

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引用次数: 0

Fully decentralized period k-times anonymous authentication with access criteria 具有访问标准的完全分散的k次匿名身份验证

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-11-19 DOI: 10.1016/j.csi.2025.104097

Hongyan Di , Yinghui Zhang , Ziqi Zhang , Yibo Pang , Rui Guo , Yangguang Tian

The explosive growth of Internet user devices highlights the strong and urgent need for digital identity infrastructure. However, the existing decentralized identity schemes are still not fully decentralized, and there is still a contradiction between publicly auditable credentials and maintaining anonymity. Therefore, using advanced cryptographic techniques such as signature proof of knowledge, Pedersen commitment, and Merkle tree, this paper propose a fully decentralized period k-times anonymous authentication with access criteria. The scheme allows user credentials to be publicly audited, users can manage their identity independently, and the verifier can not only verify the user’s identity, but also implement access control. The issuer does not need to hold a key or maintain a list, and it can still authenticate even after the trusted center is attacked, and only three zero-knowledge proofs are needed for registration and verification. The security analysis indicates that this scheme satisfies unforgeability, anonymity, unlinkability and attribute privacy. Performance evaluation shows significant improvements in both computational and communication efficiency over existing schemes.

互联网用户设备的爆炸式增长凸显了对数字身份基础设施的强烈而迫切的需求。然而，现有的去中心化身份方案仍然没有完全去中心化，公开可审计的凭证和保持匿名之间仍然存在矛盾。因此，本文利用知识签名证明、Pedersen承诺和Merkle树等先进的密码学技术，提出了一种具有访问标准的完全分散的k次匿名身份验证方法。该方案允许对用户凭证进行公开审计，用户可以独立管理自己的身份，验证者不仅可以验证用户的身份，还可以实现访问控制。发行者不需要持有密钥，也不需要维护列表，即使在可信中心受到攻击后，发行者仍然可以进行身份验证，注册和验证只需要三个零知识证明。安全性分析表明，该方案满足不可伪造性、匿名性、不可链接性和属性隐私性。性能评估表明，与现有方案相比，计算效率和通信效率都有显著提高。

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引用次数: 0

An autonomous deep reinforcement learning-based approach for memory configuration in serverless computing 无服务器计算中基于自主深度强化学习的内存配置方法

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-11-19 DOI: 10.1016/j.csi.2025.104098

Zahra Shojaee Rad, Mostafa Ghobaei-Arani, Reza Ahsan

Serverless computing has become very popular in recent years due to its cost savings and flexibility. Serverless computing is a cloud computing model that allows developers to create and deploy code without having to manage the infrastructure. It has been embraced due to its scalability, cost savings, and ease of use. However, memory configuration is one of the important challenges in serverless computing due to the transient nature of serverless functions, which are stateless and ephemeral. In this paper, we propose an autonomous approach using deep reinforcement learning and a reward mechanism for memory configuration called Auto Opt Mem. In the Auto Opt Mem mechanism, the system learns to allocate memory resources to serverless functions in a way that balances overall performance and minimizes wastage of resources. Finally, we validate the effectiveness of our solution, the findings revealed that Auto Opt Mem mechanism enhances resource utilization, reduces operation cost and latency, and improves quality of service (QoS). Our experiments demonstrate that Auto Opt Mem mechanism achieves 16.8 % lower latency compared to static allocation, 11.8 % cost reduction, and 6.8 % improve in QoS, resource utilization, and efficient memory allocation compared with base-line methods.

近年来，无服务器计算由于其节省成本和灵活性而变得非常流行。无服务器计算是一种云计算模型，它允许开发人员创建和部署代码，而无需管理基础设施。它因其可伸缩性、成本节约和易用性而受到欢迎。然而，内存配置是无服务器计算中的重要挑战之一，因为无服务器功能具有瞬时特性，它们是无状态和短暂的。在本文中，我们提出了一种使用深度强化学习和记忆配置奖励机制的自主方法，称为Auto Opt Mem。在Auto Opt内存机制中，系统学习以平衡整体性能和最小化资源浪费的方式将内存资源分配给无服务器功能。最后，我们验证了该方案的有效性，结果表明，自动选择内存机制提高了资源利用率，降低了运行成本和延迟，提高了服务质量（QoS）。我们的实验表明，与静态分配相比，自动选择内存机制的延迟降低了16.8%，成本降低了11.8%，在QoS、资源利用率和有效内存分配方面比基线方法提高了6.8%。

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引用次数: 0

Traceable signatures from lattices 来自格子的可追踪签名

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-11-13 DOI: 10.1016/j.csi.2025.104092

Nam Tran , Khoa Nguyen , Dongxi Liu , Josef Pieprzyk , Willy Susilo

Traceable signatures (Kiayas et al., 2004) is an anonymous digital signature system that extends the tracing power of opening authority in group signatures. There are many known constructions of traceable signatures but all are based on number-theoretical/pairing assumptions. For such reason, they may not be secure in the presence of quantum computers. This work revisits the notion of traceable signatures and presents a lattice-based construction provably secure in the quantum random oracle model (QROM).

可跟踪签名（Kiayas et al., 2004）是一种匿名数字签名系统，它扩展了群签名中开放权限的跟踪能力。有许多已知的可追踪签名结构，但都是基于数字理论/配对假设。因此，在量子计算机的存在下，它们可能不安全。这项工作重新审视了可追踪签名的概念，并提出了一个基于格子的构造，可以证明在量子随机预言模型（QROM）中是安全的。

引用次数: 0

Enhancing decision-making in Industry 5.0 through adaptive human–machine interfaces: A systematic literature review 通过自适应人机界面增强工业5.0中的决策：系统的文献综述

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-11-13 DOI: 10.1016/j.csi.2025.104091

Salima Meftah , M’hammed Sahnoun , Mourad Messaadia , Sidi-Mohamed Benslimane

In the dynamic landscape of Industry 5.0, adaptive human–machine interface (HMI) plays a pivotal role in shaping decision-making processes. This study constitutes a systematic literature review focusing on adaptive HMI in Industry 5.0, exploring their applications and implications within the decision-making context. The research objectives are structured around key questions, addressing the manifestation of adaptive HMI in the context of Industry 5.0, the evolutionary trajectory of HMI across industrial revolutions, relevant studies on adaptive HMI, the discernible impact of contextual use on HMI adaptation, emerging challenges of the new generation of HMI within Industry 5.0, and the integration of advanced technologies in adaptive HMI. Our findings identify key factors shaping decision-making in adaptive HMI, including acceptance and trust, transparency and reliability, privacy and security, human-centric design, long-term usability effects, accessibility, and decision support capabilities. We also highlight the role of enabling technologies—such as the Internet of Things (IoT), Internet of Humans (IoH), Virtual and Augmented Reality (VR/AR), Artificial Intelligence (AI), the Internet of Everything (IoE), and their synergistic integration in advancing adaptive HMI capabilities. This research provides valuable insights for designing and developing the next generation of adaptive HMI, specifically aimed at enhancing decision-making operators’ productivity and improving the quality of their decisions in smart manufacturing environments within Industry 5.0.

在工业5.0的动态环境中，自适应人机界面（HMI）在塑造决策过程中起着关键作用。本研究对工业5.0中的自适应人机界面进行了系统的文献综述，探讨了它们在决策环境中的应用和影响。研究目标围绕以下关键问题展开：工业5.0背景下自适应HMI的表现、HMI在工业革命中的演变轨迹、适应性HMI的相关研究、情境使用对HMI适应的明显影响、工业5.0中新一代HMI面临的新挑战，以及先进技术在适应性HMI中的整合。我们的研究结果确定了影响自适应人机界面决策的关键因素，包括接受度和信任、透明度和可靠性、隐私和安全性、以人为本的设计、长期可用性影响、可访问性和决策支持能力。我们还强调了使能技术的作用，如物联网（IoT）、人联网（IoH）、虚拟和增强现实（VR/AR）、人工智能（AI）、万物互联（IoE），以及它们在推进自适应人机界面能力方面的协同整合。本研究为设计和开发下一代自适应人机界面提供了有价值的见解，特别是针对工业5.0智能制造环境中提高决策操作员的生产力和决策质量。

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引用次数: 0

Graph-based interpretable dialogue sentiment analysis: A HybridBERT-LSTM framework with semantic interaction explainer 基于图的可解释对话情感分析：一个带有语义交互解释器的HybridBERT-LSTM框架

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-11-12 DOI: 10.1016/j.csi.2025.104086

Ercan Atagün , Günay Temür , Serdar Bi̇roğul

Conversational sentiment analysis in natural language processing faces substantial challenges due to intricate contextual semantics and temporal dependencies within multi-turn dialogues. We present a novel HybridBERT-LSTM architecture that integrates BERT’s contextualized embeddings with LSTM’s sequential processing capabilities to enhance sentiment classification performance in dialogue scenarios. Our framework employs a dual-pooling mechanism to capture local semantic features and global discourse dependencies, addressing limitations of conventional approaches. Comprehensive evaluation on IMDb benchmark and real-world dialogue datasets demonstrates that HybridBERT-LSTM consistently improves over standalone models (LSTM, BERT, CNN, SVM) across accuracy, precision, recall, and F1-score metrics. The architecture effectively exploits pre-trained contextual representations through bidirectional LSTM layers for temporal discourse modeling. We introduce WordContextGraphExplainer, a graph-theoretic interpretability framework addressing conventional explanation method limitations. Unlike LIME’s linear additivity assumptions treating features independently, our approach utilizes perturbation-based analysis to model non-linear semantic interactions. The framework generates semantic interaction graphs with nodes representing word contributions and edges encoding inter-word dependencies, visualizing contextual sentiment propagation patterns. Empirical analysis reveals LIME’s inadequacies in capturing temporal discourse dependencies and collaborative semantic interactions crucial for dialogue sentiment understanding. WordContextGraphExplainer explicitly models semantic interdependencies, negation scope, and temporal flow across conversational turns, enabling comprehensive understanding of both word-level contributions and contextual interaction influences on decision-making processes. This integrated framework establishes a new paradigm for interpretable dialogue sentiment analysis, advancing trustworthy AI through high-performance classification coupled with comprehensive explainability.

由于多回合对话中复杂的语境语义和时间依赖性，自然语言处理中的会话情感分析面临着巨大的挑战。我们提出了一种新的HybridBERT-LSTM架构，它将BERT的上下文嵌入与LSTM的顺序处理能力相结合，以增强对话场景中的情感分类性能。我们的框架采用双池机制来捕获局部语义特征和全局话语依赖，解决了传统方法的局限性。对IMDb基准和真实世界对话数据集的综合评估表明，HybridBERT-LSTM在准确性、精密度、召回率和f1分数指标上持续优于独立模型（LSTM、BERT、CNN、SVM）。该体系结构通过双向LSTM层有效地利用预训练的上下文表示进行时间话语建模。我们介绍了WordContextGraphExplainer，这是一个图论可解释性框架，解决了传统解释方法的局限性。与LIME的线性可加性假设独立处理特征不同，我们的方法利用基于微扰的分析来模拟非线性语义交互。该框架生成语义交互图，节点表示词贡献，边编码词间依赖关系，可视化上下文情感传播模式。实证分析表明，LIME在捕捉对对话情感理解至关重要的时间话语依赖和协作语义交互方面存在不足。WordContextGraphExplainer明确地对语义相互依赖、否定范围和跨会话回合的时间流进行建模，从而能够全面理解单词级别的贡献和上下文交互对决策过程的影响。这个集成框架为可解释的对话情感分析建立了一个新的范式，通过高性能分类和全面的可解释性来推进可信赖的人工智能。

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引用次数: 0

Fluent: Round-efficient secure aggregation for private federated learning Fluent：用于私有联邦学习的高效安全聚合

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-11-12 DOI: 10.1016/j.csi.2025.104096

Xincheng Li , Jianting Ning , Geong Sen Poh , Xinchun Yin , Leo Yu Zhang , Tianwei Zhang

Federated learning (FL) facilitates collaborative training of machine learning models among a large number of clients while safeguarding the privacy of their local datasets. However, FL remains susceptible to vulnerabilities such as privacy inference and inversion attacks. Single-server secure aggregation schemes were proposed to address these threats. Nonetheless, they encounter practical constraints due to their round and communication complexities. This work introduces Fluent, a round-and communication-efficient secure aggregation scheme for private FL. Fluent offers improvements compared to state-of-the-art solutions, i.e., BBGLR (Bell et al., 2020) and Flamingo (Ma et al., 2023): (1) it eliminates the need for iterative handshakes and secret sharing operations by efficiently reusing the shares across multiple training iterations, while maintaining an equivalent level of security assurance as BBGLR and Flamingo; (2) it accomplishes both the consistency check and weight unmasking in one round, thereby reducing the communication complexity. With these constructions, Fluent achieves the fewest communication rounds (i.e., two in the collection phase) in the malicious server setting, in contrast to at least three rounds in BBGLR and Flamingo. This significantly reduces the latency for geographically distributed clients; (3) In addition, a dynamic variant with a participant selection algorithm and multilevel (hierarchical) secret sharing scheme was introduced. It facilitates dynamic client joining, hence enhancing the flexibility and scalability of Fluent. We implemented Fluent and compared it with BBGLR and Flamingo. Experimental results based on the EMNIST dataset show that Fluent achieves significant enhancements in training efficiency. Specifically, Fluent achieves improvements of 6.8

\times

and 2.4

\times

in training time against BBGLR and Flamingo respectively, without compromising accuracy.

联邦学习（FL）促进了大量客户端之间机器学习模型的协作训练，同时保护了本地数据集的隐私。然而，FL仍然容易受到隐私推断和反转攻击等漏洞的影响。针对这些威胁，提出了单服务器安全聚合方案。然而，由于它们的循环和通信复杂性，它们遇到了实际的限制。本工作介绍了Fluent，一种用于私有FL的圆形和通信高效的安全聚合方案。与最先进的解决方案(即BBGLR （Bell等人，2020）和Flamingo （Ma等人，2023）相比，Fluent提供了改进：(1)通过在多个训练迭代中有效地重用共享，消除了迭代握手和秘密共享操作的需要，同时保持了与BBGLR和Flamingo相同的安全保证水平；(2)在一轮内完成一致性检查和权值揭开，降低了通信复杂度。通过这些结构，Fluent在恶意服务器设置中实现了最少的通信轮数（即在收集阶段实现了两轮），而在BBGLR和Flamingo中至少实现了三轮。这大大减少了地理上分布的客户端的延迟；(3)此外，引入了一种具有参与者选择算法和多级（分层）秘密共享方案的动态变体。它促进了动态客户端加入，从而增强了Fluent的灵活性和可扩展性。我们实现了Fluent，并将其与BBGLR和Flamingo进行了比较。基于EMNIST数据集的实验结果表明，Fluent在训练效率上有显著提高。具体来说，在不影响准确率的前提下，Fluent对BBGLR和Flamingo的训练时间分别提高了6.8倍和2.4倍。

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引用次数: 0