High-Confidence Computing最新文献

Pluggable AI-based real-time stragglers detection framework in Hadoop Hadoop中可插入的基于人工智能的实时掉队检测框架

IF 3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2025-07-03 DOI: 10.1016/j.hcc.2025.100341

Xinyuan Liu, Yinhao Li, Rajiv Ranjan, Devki Nandan Jha

The growing reliance on big data frameworks such as Hadoop has revolutionized data processing across various domains, enabling large-scale storage and distributed computation. Hadoop is widely employed in real-world applications such as high-performance computation tasks, e-commerce and data analysis in healthcare. However, the efficiency of Hadoop systems is often hampered by faults and anomalies, with stragglers emerging as one of the most prevalent issues. Stragglers disrupt workflows, waste resources and degrade system performance. While existing anomaly detection models employ methods like median analysis or static thresholds, they often struggle with issues such as high false positives, lack of adaptability and poor handling of complex heterogeneous environments. To address these challenges, this paper presents Plabs, a flexible stragglers detection framework for Hadoop. The framework comprises two core components: (1) a Monitoring Module providing real-time tracking of cluster resources and task progress and (2) a Pluggable AI-based straggler detection module, designed for precise straggler task identification. By leveraging advanced monitoring and AI-driven analysis, Plabs offers an automated, flexible and scalable solution for detecting stragglers at run-time in Hadoop clusters. We evaluated Plabs exhaustively with three Machine Learning (ML), two Deep Learning (DL) and two Large Language Models (LLMs) on five different applications in a real testbed environment. Our experiment evaluation shows that DL models outperform others in identifying Hadoop stragglers, achieving superior accuracy and reliability for all the applications.

对Hadoop等大数据框架的日益依赖已经彻底改变了跨各个领域的数据处理，使大规模存储和分布式计算成为可能。Hadoop被广泛应用于现实世界的应用程序中，如高性能计算任务、电子商务和医疗保健领域的数据分析。然而，Hadoop系统的效率经常受到故障和异常的阻碍，掉队者成为最普遍的问题之一。掉队者扰乱工作流程，浪费资源，降低系统性能。虽然现有的异常检测模型采用了中位数分析或静态阈值等方法，但它们经常会遇到误报率高、适应性不足以及对复杂异构环境处理能力差等问题。为了应对这些挑战，本文提出了Plabs，一个灵活的Hadoop掉队检测框架。该框架包括两个核心组件：(1)监控模块，提供集群资源和任务进度的实时跟踪；(2)基于Pluggable ai的掉队者检测模块，用于精确识别掉队者任务。通过利用先进的监控和人工智能驱动的分析，Plabs提供了一个自动化、灵活和可扩展的解决方案，用于在Hadoop集群的运行时检测掉队者。我们在一个真实的测试平台环境中，用三个机器学习（ML），两个深度学习（DL）和两个大型语言模型（llm）在五个不同的应用程序上对Plabs进行了详尽的评估。我们的实验评估表明，深度学习模型在识别Hadoop掉队者方面优于其他模型，为所有应用程序实现了卓越的准确性和可靠性。

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引用次数: 0

Digital twins in healthcare IoT: A systematic review 医疗物联网中的数字孪生：系统回顾

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2025-07-03 DOI: 10.1016/j.hcc.2025.100340

Md Rafiul Kabir , Fairuz Shadmani Shishir , Sumaiya Shomaji , Sandip Ray

Digital twin technology initially marked its presence in production and engineering, subsequently revolutionizing the healthcare sector with its groundbreaking applications. These include the creation of virtual replicas of patients and medical devices, enabling the formulation of personalized treatment plans. The rise of microcomputing, miniaturized hardware, and advanced machine-to-machine communications has laid the foundation for the Internet-of-Medical Things (IoMT), significantly transforming patient care through remote monitoring and timely diagnostics. Amid these technological strides, this paper offers a systematic review of digital twin technology’s integration within healthcare IoT, underlining its crucial role in promoting personalized medicine and tackling the pressing security challenges inherent in healthcare IoT systems. Focusing solely on the growing field of smart healthcare systems powered by IoT infrastructure, we explore the use of digital twins in digital patient modeling, the lifecycle of smart hospitals, surgical planning, medical devices, the pharmaceutical industry, and the IoMT cyber infrastructure, demonstrating their transformative potential in modern healthcare. Building on these findings, we outline key technical implications and emerging trends, highlight current challenges, and propose future research directions to advance healthcare IoT and its digital twin applications.

数字孪生技术最初标志着其在生产和工程领域的存在，随后以其开创性的应用彻底改变了医疗保健行业。其中包括创建患者和医疗设备的虚拟复制品，从而能够制定个性化的治疗计划。微计算、小型化硬件和先进的机器对机器通信的兴起为医疗物联网（IoMT）奠定了基础，通过远程监控和及时诊断显著改变了患者护理。在这些技术进步中，本文系统地回顾了数字孪生技术在医疗物联网中的集成，强调了其在促进个性化医疗和解决医疗物联网系统固有的紧迫安全挑战方面的关键作用。仅关注由物联网基础设施驱动的智能医疗系统这一不断增长的领域，我们探索了数字孪生在数字患者建模、智能医院生命周期、手术计划、医疗设备、制药行业和物联网网络基础设施中的应用，展示了它们在现代医疗保健中的变革潜力。在这些发现的基础上，我们概述了关键的技术影响和新兴趋势，强调了当前的挑战，并提出了未来的研究方向，以推进医疗物联网及其数字孪生应用。

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引用次数: 0

A novel zero-day ransomware detection approach based on CVAE and 1D-CNN 一种基于CVAE和1D-CNN的零日勒索软件检测方法

IF 3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2025-07-01 DOI: 10.1016/j.hcc.2025.100338

Bohan Cui , Yan Hu , Tianheng Qu , Yunhua He , Limin Sun

Ransomware has emerged as one of the most prevalent and destructive cyber attacks confronting global organizations. By locking critical devices or encrypting essential data and then demanding payment for restoration, ransomware attacks disrupt operations, result in significant financial losses, and damage organizational reputations. In particular, zero-day ransomware attacks, which attempt to exploit previously unknown vulnerabilities, pose a severe threat to existing cyber security solutions. Due to the lack of training data, detection of zero-day ransomware attacks remains a significant challenge. This paper proposes a novel zero-day ransomware detection framework that integrates a refined Conditional Variational Autoencoder (CVAE) with a 1D Convolutional Neural Network (1D-CNN). The encoder of the CVAE model comprises a posterior network and a parallel prior network. Using variational coding, the posterior network maps behavioral features of software samples from known families into a latent space, represented by a fixed multivariate Gaussian distribution with a diagonal covariance matrix. Simultaneously, the prior network eliminates dependency on class labels while maintaining distributional consistency with the posterior network via Kullback–Leibler (KL) divergence minimization. This dual-network structure enables unified latent space mapping for both labeled and unlabeled samples, effectively narrowing distributional discrepancies between software samples from known and unknown families. The harmonized latent representations subsequently enhance the discriminative capability of the 1D-CNN classifier in detecting zero-day ransomware. The comprehensive experimental results have verified that the proposed method can effectively detect zero-day ransomware attacks.

勒索软件已经成为全球组织面临的最普遍和最具破坏性的网络攻击之一。通过锁定关键设备或加密重要数据，然后要求支付恢复费用，勒索软件攻击会破坏操作，导致重大财务损失，并损害组织声誉。特别是，零日勒索软件攻击，试图利用以前未知的漏洞，对现有的网络安全解决方案构成严重威胁。由于缺乏训练数据，零日勒索软件攻击的检测仍然是一个重大挑战。本文提出了一种新的零日勒索软件检测框架，该框架集成了改进的条件变分自编码器（CVAE）和一维卷积神经网络（1D- cnn）。CVAE模型的编码器包括一个后验网络和一个并行的先验网络。使用变分编码，后验网络将来自已知家族的软件样本的行为特征映射到一个潜在空间中，该空间由具有对角协方差矩阵的固定多元高斯分布表示。同时，先验网络消除了对类标签的依赖，同时通过Kullback-Leibler （KL）散度最小化保持与后验网络的分布一致性。这种双重网络结构使标记和未标记样本的潜在空间映射统一，有效地缩小了已知和未知家族软件样本之间的分布差异。统一的潜在表征随后增强了1D-CNN分类器检测零日勒索软件的判别能力。综合实验结果验证了该方法能够有效检测零日勒索软件攻击。

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引用次数: 0

KANs-DETR: Enhancing Detection Transformer with Kolmogorov–Arnold Networks for small object kan - detr：基于Kolmogorov-Arnold网络的小目标增强检测变压器

IF 3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2025-07-01 DOI: 10.1016/j.hcc.2025.100336

Jingyu Zhang , Wentao Peng , Anyan Xiao , Tao Liu , Junchao Fu , Jian Chen , Zhuo Yan

This research proposed an end-to-end object detection network based on Kolmogorov–Arnold Networks (KANs)-Detection Transformer (DETR). KANs block was introduced into encoder–decoder structure instead of the full connection layer to dynamically learn the activation function and improve the robustness and accuracy of the model. Experiments showed that the detection capability of KANs-DETR on multicategory object detection was better than that of HGNetv2 and Swin Transformer as backbone. Furthermore, in order to solve the problem of insensitivity to small objects, the Squeeze-and-Excitation module was applied for feature fusion and presented better performance. The KANs-DETR achieved high detection accuracy and efficiency in handling small objects in complex scenes, providing a new perspective for network optimization.

本研究提出一种基于Kolmogorov-Arnold网络(KANs)-检测变压器（DETR）的端到端目标检测网络。在编解码器结构中引入KANs块代替全连接层，动态学习激活函数，提高模型的鲁棒性和准确性。实验表明，kan - detr对多类目标的检测能力优于HGNetv2和Swin Transformer作为主干的检测能力。此外，为了解决对小物体不敏感的问题，采用了Squeeze-and-Excitation模块进行特征融合，表现出更好的性能。kan - detr在复杂场景中处理小目标时实现了较高的检测精度和效率，为网络优化提供了新的视角。

引用次数: 0

Localitycache: Toward efficient straggler tolerance in LRC-coded storage via caching local parity blocks Localitycache：通过缓存本地奇偶校验块，在lrc编码存储中实现高效的离散容错

IF 3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2025-07-01 DOI: 10.1016/j.hcc.2025.100339

Ximeng Chen , Si Wu , Yinlong Xu

Modern distributed storage systems increasingly employ Locally Repairable Codes (LRCs) to provide reliable, low-cost data storage with high repair efficiency. However, the presence of stragglers, i.e., nodes that unpredictably slow down, can significantly impact access latency. Traditional approaches for handling stragglers, such as detection, blacklisting, or speculative execution, are often insufficient for efficient straggler tolerance. In this paper, we show how an in-memory caching strategy coupled with LRCs can bypass stragglers without relying on precise straggler detection. We propose LocalityCache, a novel in-memory caching mechanism designed for LRC-coded distributed storage systems, which effectively mitigates the impact of stragglers by caching local parity blocks. We provide theoretical guarantees for LocalityCache and show that caching local parity blocks minimizes the likelihood of encountering stragglers. Additionally, we devise optimized workflows for write, read, and repair operations under LocalityCache to ensure system efficiency. We implement LocalityCache in a distributed key–value store prototype atop Redis. Our extensive testbed evaluations show that LocalityCache can significantly reduce read latency of the baselines by up to 73.6% in the presence of stragglers.

现代分布式存储系统越来越多地采用本地可修复代码（lrc）来提供可靠、低成本和高修复效率的数据存储。但是，离散节点（即不可预测地变慢的节点）的存在会显著影响访问延迟。处理掉队者的传统方法，如检测、列入黑名单或推测执行，通常不足以有效地容忍掉队者。在本文中，我们展示了与lrc相结合的内存缓存策略如何绕过掉队者，而不依赖于精确的掉队者检测。我们提出了LocalityCache，这是一种为lrc编码的分布式存储系统设计的新型内存缓存机制，它通过缓存本地奇偶校验块有效地减轻了离散者的影响。我们为LocalityCache提供了理论保证，并表明缓存本地奇偶校验块可以最大限度地减少遇到掉队者的可能性。此外，我们在LocalityCache下优化了写、读和修复操作的工作流程，以确保系统效率。我们在Redis之上的分布式键值存储原型中实现了LocalityCache。我们广泛的测试平台评估表明，LocalityCache可以显着减少基线的读取延迟，在存在散点的情况下可减少高达73.6%。

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引用次数: 0

A novel approach to privacy and traceability using attribute-based signature in decentralized identifier 在去中心化标识符中使用基于属性的签名实现隐私和可追溯性的新方法

IF 3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2025-05-27 DOI: 10.1016/j.hcc.2025.100326

Taehoon Kim , Dahee Seo , Im-Yeong Lee , Su-Hyun Kim

This paper proposes a novel scheme that enhances privacy and ensures accountability by mitigating signature-based correlation risks in decentralized identifiers (DIDs). Existing DIDs often rely on traditional digital signatures, making them vulnerable to attacks that link user identities across transactions. Our proposed scheme leverages attribute-based signatures (ABS) to provide anonymous authentication, preventing such correlation and protecting user privacy. To deter the abuse of anonymity, it incorporates a traceability mechanism, enabling authorized entities to trace a user’s DID when necessary. The scheme’s security, including anonymity and traceability, is formally proven under the random oracle model.

本文提出了一种新的方案，通过减轻分散标识符（did）中基于签名的相关风险来增强隐私性并确保问责性。现有的did通常依赖于传统的数字签名，这使得它们容易受到跨事务链接用户身份的攻击。我们提出的方案利用基于属性的签名（ABS）提供匿名身份验证，防止这种相关性并保护用户隐私。为了防止滥用匿名性，它结合了可追溯机制，使授权实体能够在必要时跟踪用户的DID。在随机oracle模型下正式证明了该方案的安全性，包括匿名性和可追溯性。

引用次数: 0

A privacy-preserving class imbalance mitigation framework for face recognition 一种保护隐私的人脸识别类失衡缓解框架

IF 3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2025-05-21 DOI: 10.1016/j.hcc.2025.100325

Amani Aldahiri , Ibrahim Khalil , Mohammad Saidur Rahman , Mohammed Atiquzzaman

AI-powered face recognition has become essential to various IoT applications, including home automation, security systems, and personalized services. While these systems offer significant advancements, they still face critical challenges related to accuracy and privacy. One major issue is class imbalance, which is common in face recognition systems where certain demographic groups are underrepresented. This imbalance results in biased models, compromising the accuracy and fairness of these systems. Furthermore, traditional centralized training methods can expose sensitive facial data, raising serious privacy concerns. Federated Learning (FL) has emerged as a solution to improve model training by enabling collaboration across devices without sharing sensitive data. However, it also worsens the issue of data heterogeneity. This paper proposes a Hierarchical Federated Learning (HFL) framework to address class imbalance while preserving privacy. By aggregating local models at different hierarchical levels, the framework mitigates data imbalance and enhances fairness in face recognition systems. Additionally, a privacy-preserving mechanism based on Secure Multi-Party Computation (SMPC) is implemented to ensure data security during the training process.

人工智能面部识别已经成为各种物联网应用的关键，包括家庭自动化、安全系统和个性化服务。虽然这些系统取得了重大进步，但它们仍然面临着与准确性和隐私相关的关键挑战。一个主要问题是阶级不平衡，这在某些人口群体代表性不足的人脸识别系统中很常见。这种不平衡导致了有偏见的模型，损害了这些系统的准确性和公平性。此外，传统的集中式训练方法可能会暴露敏感的面部数据，引发严重的隐私问题。联邦学习（FL）已经成为一种解决方案，通过支持跨设备协作而不共享敏感数据来改进模型训练。然而，它也加剧了数据异构的问题。本文提出了一种层次联邦学习（HFL）框架，在保护隐私的同时解决班级不平衡问题。该框架通过对不同层次的局部模型进行聚合，减轻了数据不平衡，提高了人脸识别系统的公平性。此外，采用基于安全多方计算（SMPC）的隐私保护机制，保证训练过程中的数据安全。

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引用次数: 0

Blockchain-based inter-operator settlement system 基于区块链的运营商间结算系统

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2025-04-30 DOI: 10.1016/j.hcc.2025.100324

Shifu Zhang, Yulin Pan

Inter-network settlement is a critical mechanism for ensuring quality service and sustainable growth in the telecommunications industry. However, existing practices among operators suffer from inefficient, including manual workflows, untrustworthy data foundations, insecure dispute resolution, and insufficient accountability oversight. These challenges lead to prolonged settlement cycles, operational redundancies, and heightened risks of errors or leaks. To address these issues, we propose a blockchain-powered settlement chain framework that integrates business and technical systems to enable intelligent, trusted, and automated cross-operator settlement management. By synergizing consortium blockchain, privacy-preserving computation, and decentralized governance protocols, the framework establishes an end-to-end digital workflow covering data exchange, verification, auditing, and reconciliation. Key innovations include: (1) a multi-operator co-built consortium chain with cross-cloud networking and peer-to-peer governance; (2) a “data-available-but-invisible” auditing mechanism combining blockchain and privacy-preserving computation to ensure secure, compliant interactions; and (3) a dynamic chaincode architecture supporting real-time rule synchronization and adaptive cryptographic controls. The framework achieves full-process traceability, automated reconciliation, and enhanced financial governance while reducing reliance on manual intervention. This work provides a transformative paradigm for modernizing telecommunications settlement systems through digital trust infrastructure.

网络间结算是确保电信行业优质服务和可持续增长的关键机制。然而，运营商的现有做法存在效率低下的问题，包括手工工作流程、不可信的数据基础、不安全的争议解决以及不充分的问责监督。这些挑战导致结算周期延长、操作冗余以及错误或泄漏风险增加。为了解决这些问题，我们提出了一个区块链驱动的结算链框架，该框架集成了业务和技术系统，以实现智能，可信和自动化的跨运营商结算管理。通过协同财团区块链、隐私保护计算和去中心化治理协议，该框架建立了一个涵盖数据交换、验证、审计和协调的端到端数字工作流。关键创新包括：(1)具有跨云网络和点对点治理的多运营商共建联盟链；(2)结合区块链和隐私保护计算的“数据可用但不可见”审计机制，以确保安全、合规的交互；(3)支持实时规则同步和自适应密码控制的动态链码体系结构。该框架实现了全流程可追溯性、自动对账和增强的财务治理，同时减少了对人工干预的依赖。这项工作为通过数字信任基础设施实现电信结算系统的现代化提供了一个变革性范例。

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引用次数: 0

A low-complexity decorrelation method for PHY-based key generation 一种基于物理的密钥生成的低复杂度去相关方法

IF 3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2025-04-30 DOI: 10.1016/j.hcc.2025.100323

Gokhan Sahin

Physical layer characteristics of a wireless channel, which can be measured independently by the two communicating parties and yield near-identical results due to the channel reciprocity property, have been shown to be an important source of shared secrecy generation. Various types of channel state information (CSI), such as the received signal strength (RSS) and the channel impulse response (CIR) can be utilized for this purpose. Through periodic probing of the CSI, a continuous cycle of secret bit generation and key renewal can be maintained. However, many forms of wireless CSI inherently have substantial temporal correlation, which may hinder the secrecy generation process. Accordingly, various autocorrelation reduction (decorrelation) methods have been proposed, using either sub-sampling approaches that discard potentially valuable CSI data, or, computationally expensive transform domain approaches such as the Discrete Cosine Transform (DCT), Karhunen–Loeve Transform (KLT), and principal component analysis (PCA) that may not be feasible or desirable for resource and energy constrained devices. This paper proposes a low-complexity method for reducing the autocorrelation of the CSI measurements through a reordering of the data based on integer sequences such as the Fibonacci sequences, and applies it to various types of CSI data that represent both the individual path level CIR and the aggregate level multipath gain or RSS. We evaluate the performance of the method in three standard multipath ITU channel models. Fibonacci sequences are observed to be an effective means of decorrelating the channel measurement data, thereby eliminating the need for computationally intensive methods.

无线信道的物理层特性可以由通信双方独立测量，并且由于信道的互易性而产生几乎相同的结果，已被证明是共享保密生成的重要来源。各种类型的信道状态信息（CSI），如接收信号强度（RSS）和信道脉冲响应（CIR）可以用于此目的。通过对CSI的周期性探测，可以保持一个连续的密钥生成和密钥更新周期。然而，许多形式的无线CSI固有地具有大量的时间相关性，这可能会阻碍保密生成过程。因此，已经提出了各种自相关降低（去相关）方法，使用丢弃潜在有价值的CSI数据的子采样方法，或者使用计算昂贵的变换域方法，如离散余弦变换（DCT）， Karhunen-Loeve变换（KLT）和主成分分析（PCA），这些方法对于资源和能源受限的设备可能不可行或不可取。本文提出了一种低复杂度的方法，通过基于整数序列（如斐波那契序列）的数据重排序来降低CSI测量的自相关性，并将其应用于代表个体路径级CIR和聚合级多路径增益或RSS的各种CSI数据。我们在三种标准多径ITU信道模型中评估了该方法的性能。观察到斐波那契序列是信道测量数据去相关的有效手段，从而消除了对计算密集型方法的需要。

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引用次数: 0

FedBS: Solving data heterogeneity issue in federated learning using balanced subtasks FedBS：使用平衡子任务解决联邦学习中的数据异构问题

IF 3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2025-04-16 DOI: 10.1016/j.hcc.2025.100322

Chuxiao Su , Jing Wu , Rui Zhang , Zi Kang , Hui Xia , Cheng Zhang

Federated learning has emerged as a popular paradigm for distributed machine learning, enabling participants to collaborate on model training while preserving local data privacy. However, a key challenge in deploying federated learning in real-world applications arises from the substantial heterogeneity in local data distributions across participants. These differences can have negative consequences, such as degraded performance of aggregated models. To address this issue, we propose a novel approach that advocates decomposing the skewed original task into a series of relatively balanced subtasks. Decomposing the task allows us to derive unbiased features extractors for the subtasks, which are then utilized to solve the original task. Based on this concept, we have developed the FedBS algorithm. Through comparative experiments on various datasets, we have demonstrated that FedBS outperforms traditional federated learning algorithms such as FedAvg and FedProx in terms of accuracy, convergence speed, and robustness. The main reason behind these improvements is that FedBS addresses the data heterogeneity problem in federated learning by decomposing the original task into smaller, more balanced subtasks, thereby more effectively mitigating imbalances during model training.

联邦学习已经成为分布式机器学习的流行范例，使参与者能够在保护本地数据隐私的同时协作进行模型训练。然而，在实际应用程序中部署联邦学习的一个关键挑战来自参与者之间本地数据分布的巨大异质性。这些差异可能会产生负面后果，例如聚合模型的性能下降。为了解决这个问题，我们提出了一种新的方法，主张将倾斜的原始任务分解为一系列相对平衡的子任务。分解任务允许我们为子任务导出无偏特征提取器，然后利用这些子任务来解决原始任务。基于这个概念，我们开发了FedBS算法。通过对不同数据集的对比实验，我们已经证明FedBS在准确性、收敛速度和鲁棒性方面优于传统的联邦学习算法，如fedag和FedProx。这些改进背后的主要原因是，通过将原始任务分解为更小、更平衡的子任务，FedBS解决了联邦学习中的数据异构问题，从而更有效地减轻了模型训练期间的不平衡。

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引用次数: 0