检索结果-内蒙古大学图书馆

Transparent and Accountable Training Data Sharing in decentralized machine learning Systems

Computers, Materials & Continua 2024年第6期79卷 3805-3826页

作者： Siwan Noh Kyung-Hyune Rhee Industrial Science Technology Research Center Pukyong National UniversityBusan48513South Korea Division of Computer Engineering Pukyong National UniversityBusan48513South Korea

In decentralized machine learning(DML)systems,system participants contribute their resources to assist others in developing machine learning *** malicious contributions in DML systems is challenging,which has led to the exploration of blockchain *** leverages its transparency and immutability to record the provenance and reliability of training ***,storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational ***,current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading ***,less attention has been paid to the scenario where malicious requesters intentionally manipulate test data during evaluation to gain an unfair *** paper proposes a transparent and accountable training data sharing method that securely shares data among potentially malicious system ***,we introduce a blockchain-based DML system architecture that supports secure training data sharing through the IPFS ***,we design a blockchain smart contract to transparently split training datasets into training and test datasets,respectively,without involving system *** the system,transparent and accountable training data sharing can be achieved with attribute-based proxy *** demonstrate the security analysis for the system,and conduct experiments on the Ethereum and IPFS platforms to show the feasibility and practicality of the system.

关键词： decentralized machine learning data accountability dataset sharing

来源：评论

学校读者我要写书评

暂无评论

Secure and efficient decentralized machine learning through group-based model aggregation

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第4期27卷 3911-3925页

作者： Gonzalez, Brandon A. Mosqueda Hasan, Omar Uriawan, Wisnu Badr, Youakim Brunie, Lionel INSA Lyon Dept Comp Sci Villeurbanne France Univ Lyon CNRS INSA Lyon LIRISUMR5205 F-69621 Lyon France Penn State Univ Comp Sci & Engn Dept Malvern PA USA

In the domain of decentralized machine learning, enhancing privacy often comes at the cost of reduced efficiency or utility, and vice versa. Striking a balance between privacy, efficiency, and utility remains a challenge. In this paper, we present the Secure Group-Based Model Aggregation (SGBMA) framework for decentralized learning. SGBMA introduces a novel approach by dividing the set of participants into small groups and employing an efficient secure multiparty computation protocol to aggregate models within the groups. The adoption of a balanced binary tree topology of groups facilitates the seamless combination of models computed in the groups into a unified global model. At each training round, SGBMA achieves equal participation from each user in the global model, equivalent to federated learning. The privacy-efficiency balance can be adjusted with the size of the groups with no impact on model utility. By leveraging SGBMA, decentralized learning can be executed while ensuring privacy and making it applicable to large-scale scenarios. Our experiments show that SGBMA produces higher model utility for Independent and Identically Distributed data (IID) and comparable results as federated learning in non-IID.

关键词： decentralized machine learning Artificial neural networks Privacy-preserving machine learning Secure multiparty computation

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised decentralized machine learning With Device-to-Device Cooperation

引用

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第10期23卷 9757-9771页

作者： Jiang, Zhida Xu, Yang Xu, Hongli Wang, Zhiyuan Liu, Jianchun Qiao, Chunming Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Suzhou 215123 Peoples R China Univ Buffalo State Univ New York Dept Comp Sci & Engn Buffalo NY 14260 USA

The massive data from mobile and embedded devices have huge potential for training machine learning models. decentralized machine learning (DML) can avoid the inherent bottleneck of the parameter server (PS) by collaboratively training models in a device-to-device (D2D) fashion. However, the previous DML works often assume that the local data are fully annotated with ground-truth labels, which is unrealistic for many Internet of Things (IoT) applications. This arises a new practical DML scenario, namely semi-supervised DML, where the local data of distributed workers are partially labeled in the D2D network. The existing semi-supervised learning techniques are proposed for standalone or the PS architecture, which ignore the impact of D2D topology on the performance of semi-supervised learning. Thus, they cannot adequately leverage the unlabeled data of decentralized workers, leading to performance degradation. Herein, we propose a novel framework, called SSD, to address the problem of semi-supervised DML by exploiting D2D cooperation. The key insight behind SSD is that neighbor selection has a crucial impact on pseudo-label quality and communication overhead. In SSD, each worker adaptively selects its neighbors with high-quality models and similar data distribution under communication resource constraints, which helps to generate high-confidence pseudo-labels for local unlabeled data and further boosts the DML performance. Extensive empirical evaluations on both testbed and simulated environments show that SSD significantly outperforms other baselines.

关键词： Device-to-device communication Training machine learning Semisupervised learning Data models Computer architecture Mobile computing decentralized machine learning device-to-device cooperation Internet of Things unlabeled data

来源：评论

学校读者我要写书评

暂无评论

decentralized machine learning over the Internet 41

Decentralized Machine Learning over the Internet

引用

第41届中国控制会议

作者： Duoyu Zhu Jie Peng School of Computer Science and Engineering and Guangdong Provincial Key Laboratory of Computational Science Sun Yat-Sen University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

decentralized machine learning has attracted extensive research interest in recent *** to its centralized counterpart that relies on a server to collect and disseminate messages,decentralized machine learning relies on message passing among neighboring nodes,and thus avoids the communication bottleneck of the ***,most of the existing decentralized machine learning algorithms operate within data centers that are equipped with multiple CPUs and/or GPUs,assuming that the underlying communication networks are *** this paper,we implement decentralized parallel stochastic gradient descent(D-PSGD) over multiple computers connected with the Internet which is relatively *** cope with the inevitable packet losses caused by the Internet environment,we modify D-PSGD by allowing it to use stale *** experiments confirm the performance gain of decentralized machine learning over its centralized counterpart in terms of runtime,as well as the robustness of the modified D-PSGD to packet losses.

关键词： decentralized machine learning decentralized parallel stochastic gradient descent(D-PSGD) robustness

来源：评论

学校读者我要写书评

暂无评论

Joint Model Pruning and Topology Construction for Accelerating decentralized machine learning

引用

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第10期34卷 2827-2842页

作者： Jiang, Zhida Xu, Yang Xu, Hongli Wang, Lun Qiao, Chunming Huang, Liusheng Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Suzhou 215123 Jiangsu Peoples R China Univ Buffalo State Univ New York Dept Comp Sci & Engn Buffalo NY 14260 USA

Recently, mobile and embedded devices worldwide generate a massive amount of data at the network edge. To efficiently exploit the data fromdistributed devices, we concentrate on decentralized machine learning (DML), where the workers collaboratively train models under the peer-to-peer (P2P) setting. DML avoids the bottleneck of the parameter server (PS) by enabling the workers to exchange local models with their neighbors rather than the PS. However, DML still faces some key challenges, i.e., resource limitation, system heterogeneity, network dynamics and non-IID data. In this article, we design and implement MOTOR, an efficient DML mechanism that simultaneously addresses these challenges by applying model pruning and topology construction, thus accelerating DML. Specifically, MOTOR assigns different pruning ratios to heterogeneous workers. After model pruning, each worker will train and transmit a sub-model that fits its capabilities, reducing both computation and communication overhead. Besides, MOTOR dynamically constructs the network topology considering the time-varying network conditions and non-IID data distributions. We theoretically analyze the impact of pruning ratio and network topology on model training performance. Guided by the theoretical analysis, we develop a joint optimization algorithm for pruning ratio decision and topology construction to achieve the trade-off between resource overhead and training performance. We implement MOTOR on commercial devices and evaluate the performance with different DML tasks. Extensive experiments show that MOTOR achieves up to 4.2x speedup compared to the existing DML approaches.

关键词： Model pruning peer-to-peer topology construc-tion decentralized machine learning

来源：评论

学校读者我要写书评

暂无评论

Communication-efficient decentralized machine learning over Heterogeneous Networks 37

Communication-efficient Decentralized Machine Learning over ...

引用

37th IEEE International Conference on Data Engineering (IEEE ICDE)

作者： Zhou, Pan Lin, Qian Loghin, Dumitrel Ooi, Beng Chin Wu, Yuncheng Yu, Hongfang Univ Elect Sci & Technol China Chengdu Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781728191843

In the last few years, distributed machine learning has been usually executed over heterogeneous networks such as a local area network within a multi-tenant cluster or a wide area network connecting data centers and edge clusters. In these heterogeneous networks, the link speeds among worker nodes vary significantly, making it challenging for state-of-the-art machine learning approaches to perform efficient training. Both centralized and decentralized training approaches suffer from low-speed links. In this paper, we propose a decentralized approach, namely NetMax, that enables worker nodes to communicate via high-speed links and, thus, significantly speed up the training process. NetMax possesses the following novel features. First, it consists of a novel consensus algorithm that allows worker nodes to train model copies on their local dataset asynchronously and exchange information via peer-to-peer communication to synchronize their local copies, instead of a central master node (i.e., parameter server). Second, each worker node selects one peer randomly with a fine-tuned probability to exchange information per iteration. In particular, peers with high-speed links are selected with high probability. Third, the probabilities of selecting peers are designed to minimize the total convergence time. Moreover, we mathematically prove the convergence of NetMax. We evaluate NetMax on heterogeneous cluster networks and show that it achieves speedups of 3.7x, 3.4x, and 1.9x in comparison with the state-of-the-art decentralized training approaches Prague, Allreduce-SGD, and AD-PSGD, respectively.

关键词： distributed machine learning decentralized machine learning heterogeneous network communication efficiency

来源：评论

学校读者我要写书评

暂无评论

decentralized collaborative machine learning for protecting electricity data

引用

JOURNAL OF HIGH SPEED NETWORKS 2024年第4期30卷 557-567页

作者： Wang, Rui Liu, Xin Chang, Yingxian Ma, Lei Liu, Donglan Zhang, Hao Zhang, Fangzhe Sun, Lili Yao, Honglei Yu, Hao State Grid ShanDong Elect Power Res Inst Jinan Peoples R China State Grid Shandong Elect Power Co Dept 1 Jinan Peoples R China

In recent years, there has been a noticeable surge in electric power load due to economic development and improved living standards. The growing need for smart power solutions, such as leveraging user electricity data to forecast power peaks and utilizing power data statistics to enhance end-user services, has been on the rise. However, the misuse and unauthorized access of data have prompted stringent regulations to safeguard data integrity. This paper presents a novel decentralized collaborative machine learning framework aimed at predicting peak power loads while protecting the privacy of users' power data. In this scheme, multiple users engage in collaborative machine learning training within a peer-to-peer network free from a centralized server, with the objective of predicting peak power loads without compromising users' local data privacy. The proposed approach leverages blockchain technology and advanced cryptographic techniques, including multi-key homomorphic encryption and consistent hashing. Key contributions of this framework include the development of a secure dual-aggregate node aggregation algorithm and the establishment of a verifiable process within a decentralized architecture. Experimental validation has been conducted to assess the feasibility and effectiveness of the proposed scheme, demonstrating its potential to address the challenges associated with predicting peak power loads securely and preserving user data privacy.

关键词： Electricity data privacy protection decentralized machine learning

来源：评论

学校读者我要写书评

暂无评论

A blockchain-based decentralized machine learning framework for collaborative intrusion detection within UAVs

引用

COMPUTER NETWORKS 2021年 196卷

作者： Khan, Ammar Ahmed Khan, Muhammad Mubashir Khan, Kashif Mehboob Arshad, Junaid Ahmad, Farhan NED Univ Engn & Technol Karachi Dept Comp Sci & IT Karachi Pakistan NED Univ Engn & Technol Karachi Dept Software Engn Karachi Pakistan Birmingham City Univ Sch Comp & Digital Technol Birmingham W Midlands England Coventry Univ Inst Future Transport & Cities Syst Secur Grp Coventry W Midlands England

UAVs have numerous emerging applications in various domains of life. However, it is extremely challenging to gain the required level of public acceptance of UAVs without proving safety and security for human life. Conventional UAVs mostly depend upon the centralized server to perform data processing with complex machine learning algorithms. In fact, all the conventional cyber attacks are applicable on the transmission and storage of data in UAVs. While their impact is extremely serious because UAVs are highly dependent on smart systems that extensively utilize machine learning techniques in order to take decisions in human absence. In this regard, we propose to enhance the performance of UAVs with a decentralized machine learning framework based on blockchain. The proposed framework has the potential to significantly enhance the integrity and storage of data for intelligent decision making among multiple UAVs. We present the use of blockchain to achieve decentralized predictive analytics and present a framework that can successfully apply and share machine learning models in a decentralized manner. We evaluate our system using collaborative intrusion detection as a case-study in order to highlight the feasibility and effectiveness of using blockchain based decentralized machine learning approach in UAVs and other similar applications.

关键词： Unmanned aerial vehicles UAV Blockchain machine learning decentralized machine learning Collaborative intrusion detection

来源：评论

学校读者我要写书评

暂无评论

decentralized and Incentivized Federated learning: A Blockchain-Enabled Framework Utilising Compressed Soft-Labels and Peer Consistency

引用

IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第4期17卷 1449-1464页

作者： Witt, Leon Zafar, Usama Shen, Kuoyeh Sattler, Felix Li, Dan Wang, Songtao Samek, Wojciech Tsinghua Univ Beijing 100190 Peoples R China Fraunhofer Heinrich Hertz Inst Dept Artificial Intelligence D-10587 Berlin Germany Uppsala Univ Dept Informat Technol S-75236 Uppsala Sweden Zhongguancun Lab Beijing 100190 Peoples R China Tech Univ Berlin Dept Elect Engn & Comp Sci D-10587 Berlin Germany BIFOLD Berlin Inst Fdn Learning & Data D-10587 Berlin Germany

Federated learning (FL) has emerged as a powerful paradigm in Artificial Intelligence, facilitating the parallel training of Artificial Neural Networks on edge devices while safeguarding data privacy. Nonetheless, to encourage widespread adoption, Federated learning Frameworks (FLFs) must tackle (i) the power imbalance between a central authority and its participants, and (ii) the challenge of equitably measuring and incentivizing contributions. Existing approaches to decentralize and incentivize FL processes are hindered by (i) computational overhead and (ii) uncertainty in contribution assessment (Witt et al. 2023), limiting FL's scalability beyond use cases where trust between participants and the server is established. This work introduces a cutting-edge, blockchain-enabled federated learning framework that incorporates Federated Knowledge Distillation (FD) with compressed 1-bit soft-labels, aggregated through a smart contract. Furthermore, we present the Peer Truth Serum for Federated Distillation (PTSFD), which cultivates an incentive-compatible ecosystem by rewarding honest participation based on an implicit yet effective comparison of worker contributions. The primary innovation stems from its lightweight architecture that simultaneously promotes decentralization and incentivization, addressing critical challenges in contemporary FL approaches.

关键词： Blockchains Servers Predictive models Training Computational modeling Computer architecture Smart contracts Federated learning blockchain reward mechanism federated distillation decentralized machine learning

来源：评论

学校读者我要写书评

暂无评论

Consensus-Driven Hyperparameter Optimization for Accelerated Model Convergence in decentralized Federated learning

INTERNET OF THINGS

引用

INTERNET OF THINGS 2025年 30卷

作者： Khan, Anam Nawaz Khan, Qazi Waqas Rizwan, Atif Ahmad, Rashid Kim, Do Hyeun Jeju Natl Univ Dept Comp Engn Jejusi 63243 Jeju South Korea Jeju Natl Univ Bigdata Res Ctr Jeju 63243 Jeju South Korea Kyung Hee Univ Dept Elect Engn Yongin 17104 South Korea Sohar Univ Fac Comp & IT Sohar 311 Oman Jeju Natl Univ Dept Comp Engn Jejusi 63243 Jeju South Korea Jeju Natl Univ Adv Technol Res Inst Jejusi 63243 Jeju South Korea

decentralized Federated learning (DFL) enables collaborative model training across multiple devices without relying on a central server, preserving data privacy and achieving full decentralization. However, optimizing hyperparameters (HPs) in DFL presents significant challenges due to system and statistical heterogeneity and the lack of centralized coordination. Existing Hyperparameter Optimization (HPO) methods for Federated learning (FL) typically rely on a central server, making them unsuitable for fully decentralized environments. These methods face scalability issues, high communication and computation overhead, and limited adaptability to node-specific requirements. The need for serverless, fully decentralized HPO is particularly critical in scenarios demanding minimal communication and efficient resource usage. To address these challenges, we propose the single-pass decentralized Federated Hyperparameter Optimization framework (DFed-HPO), which integrates three advanced HPO strategies for computation and communication-efficient HP optimization. DFed-HPO includes three hyperparameter aggregation mechanisms: MetaRegress Aggregator (MA), Consensus Aggregator (CA), and Fusion Aggregator (FA). MA uses meta-learning principles to predict the performance of new configurations with minimal communication, while CA and FA enhance optimization and aggregation based on model similarity and consensus-building among nodes. By conducting HPO in a single pass, DFed-HPO reduces communication overhead while achieving robust hyperparameter consensus. We validated DFed-HPO on MNIST and the Electricity dataset, demonstrating its potential in energy management applications, including green hydrogen production using renewable energy systems. Results show that DFed-HPO improves model performance, accelerates convergence, and adapts to non-IID data, offering a scalable and efficient solution for decentralized AI networks in resource-constrained settings.

关键词： decentralized machine learning Model optimization Model hyperparameters Artificial intelligence decentralized learning optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：