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Compression Analysis for BlueField-2/-3 Data Processing Units: Lossy and Lossless Perspectives

IEEE MICRO 2024年第2期44卷 8-19页

作者： Li, Yuke Kashyap, Arjun Guo, Yanfei Lu, Xiaoyi Univ Calif Merced Dept Comp Sci & Engn Parallel & Distributed Syst Lab Merced CA 95343 USA Argonne Natl Lab Lemont IL 60439 USA Univ Calif Merced Dept Comp Sci & Engn Merced CA 95343 USA

A data processing unit (DPU) with programmable smart network interface card containing system-on-chip (SoC) cores is now a valuable addition to the host CPU, finding use in high-performance computing (HPC) and data center clusters for its advanced features, notably, a hardware-based data compression engine (C-engine). With the convergence of big data, HPC, and machine learning, data volumes burden communication and storage, making efficient compression vital. This positions DPUs as tools to accelerate compression workloads and enhance data-intensive applications. This article characterizes lossy (e.g., SZ3) and lossless (e.g., DEFLATE, lz4, and zlib) compression algorithms using seven real-world datasets on Nvidia BlueField-2/-3 DPUs. We explore the potential opportunities for offloading these compression workloads from the host. Our findings demonstrate that the C-engine within the DPU can achieve up to 26.8x speedup compared to its SoC core. We also provide insights on harnessing BlueField for compression, presenting seven crucial takeaways to steer future compression research with DPUs.

关键词： Engines Task analysis Data compression Throughput Memory management Hardware acceleration distributed databases

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ISFL: Federated Learning for Non-i.i.d. Data With Local Importance Sampling

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IEEE INTERNET OF THINGS JOURNAL 2024年第16期11卷 27448-27462页

作者： Zhu, Zheqi Shi, Yuchen Fan, Pingyi Peng, Chenghui Letaief, Khaled B. Tsinghua Univ Dept Elect Engn Beijing 10084 Peoples R China Huawei Technol Wireless Technol Lab Shenzhen 518129 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

As a promising learning paradigm integrating computation and communication, federated learning (FL) proceeds the local training and the periodic sharing from distributed clients. Due to the non-i.i.d. data distribution on clients, FL model suffers from the gradient diversity, poor performance, bad convergence, etc. In this work, we aim to tackle this key issue by adopting importance sampling (IS) for local training. We propose importance sampling FL(ISFL), an explicit framework with theoretical guarantees. First, we derive the convergence theorem of ISFL to involve the effects of local IS. Then, we formulate the problem of selecting optimal IS weights and obtain the theoretical solutions. We also employ a water-filling method to calculate the IS weights and develop the ISFL algorithms. The experimental results on CIFAR-10 fit the proposed theorems well and verify that ISFL reaps better performance, convergence, sampling efficiency, as well as explainability on the non-i.i.d. data. To the best of our knowledge, ISFL is the first non-i.i.d. FL solution from the local sampling aspect which exhibits theoretical compatibility with neural network models. Furthermore, as a local sampling approach, ISFL can be easily migrated into the other emerging FL frameworks.

关键词： Training Monte Carlo methods Data models Convergence Federated learning distributed databases Deep learning Federated learning (FL) importance sampling (IS) non i.i.d data water-filling optimization

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An Adaptive Federated Fuzzy C-Means Clustering With Nonindependently and Identically distributed Data

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 4015-4028页

作者： Li, Longmei Lu, Wei Pedrycz, Witold Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Liaoning Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Macau Univ Sci & Technol Inst Syst Engn Taipa Peoples R China Istinye Univ Res Ctr Performance & Prod Anal TR-34396 Istanbul Turkiye

Federated Fuzzy C-Means (FCM) has received considerable attention due to the increasing need for privacy-conscious data analysis across diverse domains and sources in many real-world applications. Recent developments in federated FCM, however, are still in their infancy and largely unexplored. These methods struggle to handle nonindependent and identically distributed (non-iid) data. Moreover, critical hyperparameters, such as the number of iterations for local updates, are typically set manually, which can significantly affect the performance of federated clustering. To address these challenges, we introduce an Adaptive Federated FCM with an auxiliary model, named AF-FCM. In this approach, prior information from the auxiliary model, along with a proximal term in the local objective, mitigates the effects of the non-iid environment, enhancing both model robustness and effectiveness. Critical hyperparameters are adaptively adjusted using a proposed adaptive particle swarm optimization (APSO) algorithm, guided by a carefully designed fitness function. Within APSO, a nonlinear regression function adjusts the inertia weight, reducing the risk of convergence to local optima. In AF-FCM, global prototypes are refined using momentum gradient descent (MGD). Numerical experiments highlight the effectiveness and generalization performance of AF-FCM across various conditions, including heterogeneity variations, the number of clients, and the number of clusters. Comparative analysis against state-of-the-art federated clustering baseline methods validates the competitive performance of AF-FCM.

关键词： Prototypes Servers Adaptation models distributed databases Data privacy Training Linear programming Fuzzy logic Computational modeling Protocols distributed learning federated learning (FL) fuzzy clustering nonindependent and identically distributed (non-iid)

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An Efficient Sequential Decentralized Federated Progressive Channel Pruning Strategy for Smart Grid Electricity Theft Detection

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第3期21卷 2393-2402页

作者： Guo, Fanghong Li, Shengwei Yang, Hao Dong, Chen Chen, Yifang Li, Guoqi Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou 310016 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

This article aims to develop a lightweight, decentralized federated learning (FL)-based strategy for electricity theft detection (ETD). Different from most of the existing ETD solutions, which typically deploy centralized deep learning models, our proposed method utilizes well-pruned lightweight networks and operates in a completely decentralized manner while maintaining the performance of the ETD model. Specifically, to protect data privacy, a novel sequential decentralized FL (SDFL) framework was designed, eliminating the centralized parameter aggregation node in traditional FL. Each client communicates model parameters only with its neighbors and trains its model locally. In addition, to facilitate deployment on edge devices, model pruning techniques are integrated with the sequential transmission characteristics of the SDFL framework. A progressive channel pruning technique is proposed, gradually reducing the number of model channels during training to promote model compression and simplify field deployment. Experiments demonstrate that our strategy compressed the model floating point operations from 18.32 to 3.60M and reduced the number of parameters from 8.61 to 3.47M, while protecting user privacy, and maintaining good performance. Deployment results on the edge devices, i.e., Raspberry Pi, indicate that our proposed strategy reduces the model inference time from 329.35 to 141.50 s, enhancing the detection efficiency by 57.04%.

关键词： Data models Computational modeling Training Servers Biological system modeling Data privacy Electricity Adaptation models Costs distributed databases Decentralized federated learning (FL) electricity theft detection (ETD) model pruning privacy protection smart grid

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Task-Aware Service Placement for distributed Learning in Wireless Edge Networks

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2025年第4期36卷 731-744页

作者： Cong, Rong Zhao, Zhiwei Wang, Mengfan Min, Geyong Liu, Jiangshu Mo, Jiwei Univ Elect Sci & Technol China UESTC Chengdu 610051 Sichuan Peoples R China Univ Elect Sci & Technol China Coll Comp Sci & Engn Chengdu 610051 Sichuan Peoples R China Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4PY England

Machine learning has been a driving force in the evolution of tremendous computing services and applications in the past decade. Traditional learning systems rely on centralized training and inference, which poses serious privacy and security concerns. To solve this problem, distributed learning over wireless edge networks (DLWENs) emerges as a trending solution and has attracted increasing research interests. In DLWENs, corresponding services need to be placed onto the edge servers to process the distributed tasks. Apparently, different placement of training services can significantly affect the performance of all distributed learning tasks. In this article, we propose TASP, a task-aware service placement scheme for distributed learning in wireless edge networks. By carefully considering the structures (directed acyclic graphs) of the distributed learning tasks, the fine-grained task requests and inter-task dependencies are incorporated into the placement strategies to realize the parallel computation of learning services. We also exploit queuing theory to characterize the dynamics caused by task uncertainties. Extensive experiments based on the Alibaba ML dataset show that, compared to the state-of-the-art schemes, the proposed work reduces the overall delay of distributed learning tasks by 38.6% on average.

关键词： Servers Distance learning Computer aided instruction Wireless communication Training Edge computing distributed databases Delays Data models Computational modeling distributed learning edge computing service placement task-aware

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Identification and Alleviation of False Data Injection Within the Cyber Layer of an Enhanced distributed Secondary Control in DC Islanded Microgrids

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IEEE ACCESS 2025年 13卷 48605-48624页

作者： Mosaad, Nada Abdel-Rahim, Omar Rohouma, Wesam Abdelkader, Sobhy Mohamed Egypt Japan Univ Sci & Technol E JUST Elect Power Engn New Borg El Arab City 21934 Alexandria Egypt Aswan Univ Fac Engn Elect Engn Dept Aswan 81542 Egypt Univ Doha Sci & Technol Elect Engn Dept Doha Qatar Mansoura Univ Fac Engn Elect Engn Dept Mansoura 35516 Egypt

As a result of the integration of communication links within secondary control to circumvent the drawbacks associated with centralized control, microgrids have evolved into cyber-physical systems which are susceptible to cybersecurity risks. This research addresses the repercussions of injecting fake data into the cyber layer within the framework of distributed secondary control in a DC-islanded microgrid. Kullback-Leibler divergence, also referred to as relative entropy, serves as a statistical metric between two probability distributions employed to compute a specific parameter tasked with mitigating the impact on the control system. This parameter comprises the multiplication of two components. The first component is an internal component derived from neighboring entities and provides insight into their security status, indicating whether they have been subjected to attacks. The second component is an external component computed locally and verifies the accuracy of the status received from each neighbor, ensuring its authenticity. Two medium voltage DC microgrids were examined through MATLAB Simulink simulations to corroborate the efficiency of the suggested approach in identifying and mitigating the consequences of false data injection. The results demonstrated that the cybersecurity threat's influence could be proficiently detected and alleviated in both microgrid configurations, while maintaining the effectiveness of secondary control functions, which are, preserving voltage regulation and precise current distribution. Finally, one of the Microgrids was implemented using OPAL-RT, allowing for the observation of output voltages and currents on a physical hardware oscilloscope.

关键词： Microgrids distributed databases distributed power generation Voltage control Decentralized control Mathematical models MATLAB Stability analysis Renewable energy sources Power system stability distributed secondary control false data injection Kullback-Leibler divergence MATLAB Simulink OPAL-RT

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Federated Multi-View K-Means Clustering

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第4期47卷 2446-2459页

作者： Yang, Miin-Shen Sinaga, Kristina P. Chung Yuan Christian Univ Dept Appl Math Taoyuan 32023 Taiwan

The increasing effect of Internet of Things (IoT) unlocks the massive volume of the availability of Big Data in many fields. Generally, these Big Data may be in a non-independently and identically distributed fashion (non-IID). In this paper, we have contributions in such a way enable multi-view k-means (MVKM) clustering to maintain the privacy of each database by allowing MVKM to be operated on the local principle of clients' multi-view data. This work integrates the exponential distance to transform the weighted Euclidean distance on MVKM so that it can make full use of development in federated learning via the MVKM clustering algorithm. The proposed algorithm, called a federated MVKM (Fed-MVKM), can provide a whole new level adding a lot of new ideas to produce a much better output. The proposed Fed-MVKM is highly suitable for clustering large data sets. To demonstrate its efficient and applicable, we implement a synthetic and six real multi-view data sets and then perform Federated Peter-Clark in Huang et al. 2023 for causal inference setting to split the data instances over multiple clients, efficiently. The results show that shared-models based local cluster centers with data-driven in the federated environment can generate a satisfying final pattern of one multi-view data that simultaneously improve the clustering performance of (non-federated) MVKM clustering algorithms.

关键词： Clustering algorithms Federated learning distributed databases Data models Data privacy Machine learning algorithms Kernel Internet of Things Servers Training data Clustering K-means multi-view data multi-view k-means (MVKM) federated learning federated MVKM privacy

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ACMFed: Fair Semi-Supervised Federated Learning With Additional Compromise Model

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IEEE ACCESS 2025年 13卷 47734-47747页

作者： Kim, Dohyoung Lee, Kangyoon Lee, Youngho Woo, Hyekyung Gachon Univ Dept IT Convergence Engn Seongnam Si 13120 South Korea Gachon Univ Dept Comp Engn Seongnam Si 13120 South Korea Kongju Natl Univ Dept Hlth Adm Gongju Si 32588 South Korea

One of the major drawbacks of federated learning (FL) is data imbalance and uneven reliability of clients, which adversely impacts model performance and generalization ability. Among these problems, data imbalance can be addressed through semi-supervised federated learning (SSFL), in which fully labeled, and fully unlabeled clients are jointly trained to derive a global model. Existing approaches work well when local clients have independent identically distributed (IID) data but fail to generalize under a more practical SSFL setting, namely fairness, non-IID. Since the non-IID problem stems from unfairness, to address this issue, we propose ACMFed, a fair and improved concurrent federated learning method that balances client connections, computing resources, data quality and quantity based on fairness. ACMFed reaches the compromise by performing fair multiple sampling of clients in models with largely deviating labeled and unlabeled clients. Each client distills the model through a multi-sub-sampling process without aggregating the local model to the central server, and builds a global model based on it. The additional compromise model applies a fair adjustment method of the new distance-based dynamic model to reduce data bias and optimize performance. ACMFed outperforms other federated learning methods and achieves faster convergence on six benchmark datasets. To code is available at https://***/kimdohyoung96/ACMFed.

关键词： Data models Federated learning Computational modeling Servers Deep learning Data privacy Training Standards Solid modeling distributed databases distributed computing methodologies fair federated learning multi-sampling protected health information semi-supervised learning

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A Hybrid Brain Tumor Classification Using FL With FedAvg and FedProx for Privacy and Robustness Across Heterogeneous Data Sources

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IEEE ACCESS 2025年 13卷 57705-57719页

作者： Sivakumar, N. Khan, Ahmad Raza Umar, Syed Ravikumar, R. N. Bremnavas, I. Lunagaria, Munindra Vaghela, Krunal Tejani, Ghanshyam G. Sharma, Sunil Kumar Marwadi Univ Dept Comp Engn Rajkot 360003 India Majmaah Univ Coll Comp & Informat Sci Dept Informat Technol Majmaah 11952 Saudi Arabia Jain Univ Sch CS & IT Bengaluru 560069 India Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Dent Coll & Hosp Dept Res Analyt Chennai 600077 India Yuan Ze Univ Dept Ind Engn & Management Taoyuan 320315 Taiwan Majmaah Univ Coll Comp & Informat Sci Dept Informat Syst Majmaah 11952 Saudi Arabia

Data privacy and heterogeneity among healthcare settings present fundamental challenges to machine learning (ML) brain tumor classification (BTC) model development based on local data. In this paper, we outline the need to develop an effective brain tumor diagnosis model while ensuring data security. We propose a distributed learning framework based on Federated Learning (FL) to build such a system. This framework combines Federated Averaging (FedAvg) and Federated Proximal (FedProx) to train Convolutional Neural Networks (CNN) on data hosted by multiple clients. FedAvg saves these locally trained models from the patients to make a global model while keeping sensitive patient data decentralized and private. Nevertheless, traditional FL suffers from model instability and divergence due to the disparity of data distributions among clients. FedProx addresses this challenge by introducing a proximal regularization term that penalizes divergence from the global model to stabilize local training considering data heterogeneity. In this work, we utilized the Brain Tumor MRI Dataset from Kaggle and trained the model to classify four brain tumors (BT), namely glioma, meningioma, pituitary tumor, and no-tumor class. Experiments were conducted for ten communication rounds, with each client training the model independently based on its local dataset and without sharing data. Results revealed that the method consistently outperformed accuracy, recall and F1-score, further validating the capabilities of the proposed framework in effectively handling data heterogeneity while conserving data privacy. The global model outperformed models that had been centralized and trained on a smaller, common dataset observed in FL scenarios to improve the potential for collaborative medical diagnosis. This method is especially suitable for medical applications with strict privacy regulations, as it provides an approach to linking heterogeneous data from multiple healthcare institutions.

关键词： Data models Data privacy Medical services Training Computational modeling Brain modeling Servers Brain tumors Accuracy distributed databases Federated learning brain tumor classification FedAvg FedProx data heterogeneity privacy protection machine learning in healthcare medical imaging

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Object Proxy Patterns for Accelerating distributed Applications

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2025年第2期36卷 253-265页

作者： Pauloski, J. Gregory Hayot-Sasson, Valerie Ward, Logan Brace, Alexander Bauer, Andre Chard, Kyle Foster, Ian Univ Chicago Dept Comp Sci Chicago IL 60637 USA Argonne Natl Lab Data Sci & Learning Div Lemont IL 60439 USA IIT Dept Comp Sci Chicago IL 60616 USA

Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fits-all support for advanced data flow patterns leaves optimization to the application programmer-optimization that becomes more difficult as data become larger. The transparent object proxy, which provides wide-area references that can resolve to data regardless of location, has been demonstrated as an effective low-level building block in such situations. Here we propose three high-level proxy-based programming patterns-distributed futures, streaming, and ownership-that make the power of the proxy pattern usable for more complex and dynamic distributed program structures. We motivate these patterns via careful review of application requirements and describe implementations of each pattern. We evaluate our implementations through a suite of benchmarks and by applying them in three meaningful scientific applications, in which we demonstrate substantial improvements in runtime, throughput, and memory usage.

关键词： distributed databases Genomics Computational modeling Production facilities Engines Connectors Programming Memory management Load modeling Complexity theory distributed computing futures memory management open-source software streaming

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