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An Efficient Federated Multiview Fuzzy C-Means Clustering Method

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 1886-1899页

作者： Hu, Xingchen Qin, Jindong Shen, Yinghua Pedrycz, Witold Liu, Xinwang Liu, Jiyuan Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Wuhan Univ Technol Sch Management Wuhan 430070 Peoples R China Chongqing Univ Sch Econ & Business Adm Chongqing 400044 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Fac Engn & Nat Sci Dept Comp Engn TR-34396 Sariyer Istanbul Turkiye

Multiview clustering has been received considerable attention due to the widespread collection of multiview data from diverse domains and sources. However, storing multiview data across multiple devices in many real scenarios poses significant challenges for efficient data analysis. Federated learning framework enables collaborative machine learning on distributed devices while preserving privacy constraints. Even though there have been intensive algorithms on multiview fuzzy clustering, federated multiview fuzzy clustering has not been adequately investigated so far. In this study, we first develop the federated learning mode into multiview fuzzy clustering and realize the federated optimization procedure, called federated multiview fuzzy C-means clustering. Then, we design an original strategy of consensus prototype learning during federated multiview fuzzy clustering. It is termed as federated multiview fuzzy C-means consensus prototypes clustering (FedMVFPC). We also further develop the federated alternative optimization algorithm with proven convergence. This study also introduces the notion of clustering prototype communication within the federated learning framework, and integrates the clustering prototypes of different views into a unified optimization formulation. The experimental studies on various benchmark datasets demonstrate that the proposed FedMVFPC method improves the federated clustering performance and efficiency. It achieves comparable or better clustering performance against the existing state-of-the-art multiview clustering algorithms.

关键词： Prototypes Fuzzy logic Clustering algorithms Clustering methods Optimization Federated learning distributed databases Fuzzy clustering federated learning multiview clustering distributed data prototype learning

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PDSR: A Privacy-Preserving Diversified Service Recommendation Method on distributed Data

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2733-2746页

作者： Wang, Lina Yang, Huan Shen, Yiran Liu, Chao Qi, Lianyong Cheng, Xiuzhen Li, Feng Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Shandong Univ Sch Software Jinan 250101 Peoples R China Ocean Univ China Dept Comp Sci & Technol Qingdao 266100 Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China

The last decade has witnessed a tremendous growth of service computing, while efficient service recommendation methods are desired to recommend high-quality services to users. It is well known that collaborative filtering is one of the most popular methods for service recommendation based on QoS, and many existing proposals focus on improving recommendation accuracy, i.e., recommending high-quality redundant services. Nevertheless, users may have different requirements on QoS, and hence diversified recommendation has been attracting increasing attention in recent years to fulfill users' diverse demands and to explore potential services. Unfortunately, the recommendation performances relies on a large volume of data (e.g., QoS data), whereas the data may be distributed across multiple platforms. Therefore, to enable data sharing across the different platforms for diversified service recommendation, we propose a Privacy-preserving Diversified Service Recommendation (PDSR) method. Specifically, we innovate in leveraging the Locality-Sensitive Hashing (LSH) mechanism such that privacy-preserved data sharing across different platforms is enabled to construct a service similarity graph. Based on the similarity graph, we propose a novel accuracy-diversity metric and design a 2-approximation algorithm to select $K$K services to recommend by maximizing the accuracy-diversity measure. Extensive experiments on real datasets are conducted to verify the efficacy of our PDSR method.

关键词： Quality of service Privacy Accuracy Soft sensors distributed databases Proposals Collaborative filtering recommendation diversity privacy preservation

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FlexRaft: Exploiting Flexible Erasure Coding for Minimum-Cost Consensus and Fast Recovery

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2024年第10期35卷 1826-1840页

作者： Zhang, Mi Kang, Qihan Lee, Patrick P. C. Chinese Acad Sci Inst Comp Technol Res Ctr Adv Comp Syst State Key Lab Processors Beijing 100045 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Inst Comp Technol Res Ctr Adv Comp Syst Beijing 101408 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China

Consensus protocols like Paxos and Raft provide data consistency and fault tolerance for distributed services. Log replication in these protocols can be supported by erasure coding, which incurs lower redundancy than full-copy replication and significantly saves network and storage costs for overall performance improvements. However, existing consensus protocols with erasure coding cannot achieve the minimum network and storage costs during log replication. We propose FlexRaft, which dynamically varies the coding scheme used in Raft based on the server status to always achieve the theoretically minimum redundancy ratio, while maintaining the same liveness as in Raft. To address the issue of an inconsistent coding scheme between the leader and its followers, we specify the prerequisite of overwriting a log entry and also allow the leader and its followers to exactly track the coding scheme being used. We further extend FlexRaft into FlexRaft+, which provides a different storage layout to vary the coding scheme through a novel technique called re-encoding-free replication, so as to enable fast server recovery. We prove that both FlexRaft and FlexRaft+ maintain Raft safety. We implement a prototype of FlexRaft and FlexRaft+, atop which we build a distributed key-value store to show its efficacy. Experiments on Alibaba Cloud show that FlexRaft achieves the theoretically minimum network and storage costs in practice, and reduces the commit latency by 44.51% and 19.37% compared with state-of-the-art CRaft and HRaft, respectively. FlexRaft+ further reduces the commit latency when the coding scheme is being varied and improves the server recovery performance.

关键词： Servers Encoding Costs Redundancy Consensus protocol Safety distributed databases Consensus erasure coding

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A Communication-Efficient Hierarchical Federated Learning Framework via Shaping Data Distribution at Edge

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第3期32卷 2600-2615页

作者： Deng, Yongheng Lyu, Feng Xia, Tengxi Zhou, Yuezhi Zhang, Yaoxue Ren, Ju Yang, Yuanyuan Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRist Dept Comp Sci & Technol Beijing 100084 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Zhongguancun Lab Beijing 100084 Peoples R China SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

Federated learning (FL) enables collaborative model training over distributed computing nodes without sharing their privacy-sensitive raw data. However, in FL, iterative exchanges of model updates between distributed nodes and the cloud server can result in significant communication cost, especially when the data distributions at distributed nodes are imbalanced with requiring more rounds of iterations. In this paper, with our in-depth empirical studies, we disclose that extensive cloud aggregations can be avoided without compromising the learning accuracy if frequent aggregations can be enabled at edge network. To this end, we shed light on the hierarchical federated learning (HFL) framework, where a subset of distributed nodes can play as edge aggregators to support edge aggregations. Under the HFL framework, we formulate a communication cost minimization (CCM) problem to minimize the total communication cost required for model learning with a target accuracy by making decisions on edge aggragator selection and node-edge associations. Inspired by our data-driven insights that the potential of HFL lies in the data distribution at edge aggregators, we propose ShapeFL, i.e., SHaping dAta distRibution at Edge, to transform and solve the CCM problem. In ShapeFL, we divide the original problem into two sub-problems to minimize the per-round communication cost and maximize the data distribution diversity of edge aggregator data, respectively, and devise two light-weight algorithms to solve them accordingly. Extensive experiments are carried out based on several opened datasets and real-world network topologies, and the results demonstrate the efficacy of ShapeFL in terms of both learning accuracy and communication efficiency.

关键词： Costs Data models Servers Computational modeling Training data Federated learning distributed databases Hierarchical federated learning communication efficiency edge computing distributed edge intelligence

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A Low-Voltage Area-Efficient TSV I/O With QEC Realizing Data Rate up to 15 Gb/s for TSV Interface

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 2024年第6期60卷 2215-2225页

作者： Kim, Taeryeong Kim, Ji-Young You, Jeonghyeok Chae, Hohyun Moon, Byoung Mo Sohn, Kyomin Jung, Seong-OoK Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea Samsung Elect Memory Div DRAM Design Team Hwaseong 18448 South Korea Samsung Elect Memory Div FLASH & DRAM Design Team Hwaseong 18448 South Korea Articron Inc Seoul 02447 South Korea

In this study, a low-voltage and area-efficient through-silicon via (TSV) I/O with a quadrature error corrector (QEC) is proposed in a 65-nm CMOS process for the TSV interface in high-bandwidth memory (HBM). As data rates and the number of stacked TSVs increase, signal integrity is significantly degraded at low-voltage operation due to the following factors: insufficient timing margins in serializer and equalizer, insufficient eye-margin in TSV signaling, and clock (CK) mismatches. To ensure sufficient timing margins, a 4:1 multiplexing (MUX) output driver with an overlapped MUX (OVM) technique is adopted in the transmitter (TX). To improve eye-margin, a hybrid equalizer and direct-feedback one-tap decision feedback equalizer (DFE) are adopted in the TX and receiver (RX), respectively. CK mismatches are calibrated through the proposed QEC. A 12-stacked TSVs channel is emulated using six metal layers in the 65 nm CMOS process. The measured energy efficiency is 0.137-0.145 pJ/b/pF at 8-15 Gb/s data rates, respectively, with 0.6-0.7 V supply voltage.

关键词： Through-silicon vias Low voltage Decision feedback equalizers Logic gates Voltage Random access memory Degradation Delays Noise distributed databases High-bandwidth memory (HBM) interface low-supply voltage low-swing signaling quadrature error corrector (QEC) through-silicon-via (TSV) I/O

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Low-Power Scalable TSPI: A Modular Off-Chip Network for Edge AI Accelerators

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IEEE ACCESS 2024年 12卷 141448-141459页

作者： Park, Seunghyun Park, Daejin Kyungpook Natl Univ Sch Elect & Elect Engn Daegu 41566 South Korea Kyungpook Natl Univ Sch Elect Engn Daegu 41566 South Korea

In this paper, we present a novel off-chip network architecture, the Tile Serial Peripheral Interface (TSPI), designed for low-power, scalable edge AI accelerators. Our approach modifies the conventional SPI to support a modular network structure that facilitates the scalable connection of multiple accelerators. The TSPI network employs a subset mapping algorithm for efficient routing and integrates the message passing interface (MPI) protocol to ensure rapid data distribution and aggregation. This modular architecture significantly reduces power consumption and improves processing speed. Experimental results demonstrate that our proposed TSPI network achieves a 54.7% reduction in power consumption and an 82.3% decrease in switching power compared to traditional SPI networks, along with a 23% increase in processing speed when utilizing 16 nodes. These advancements make the TSPI network an effective solution for enhancing AI performance in edge computing environments.

关键词： Random access memory Computational modeling Performance evaluation Edge AI distributed databases Scalability Indexes Low power electronics Network architecture Accelerator architectures TSPI off-chip network edge device subset mapping algorithm low power MPI

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MementoHash: A Stateful, Minimal Memory, Best Performing Consistent Hash Algorithm

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第4期32卷 3528-3543页

作者： Coluzzi, Massimo Brocco, Amos Antonucci, Alessandro Leidi, Tiziano Univ Appl Sci & Arts Southern Switzerland Dept Innovat Technol CH-6962 Lugano Viganello Switzerland

Consistent hashing is used in distributed systems and networking applications to spread data evenly and efficiently across a cluster of nodes. In this paper, we present MementoHash, a novel consistent hashing algorithm that eliminates known limitations of state-of-the-art algorithms while keeping optimal performance and minimal memory usage. We describe the algorithm in detail, provide a pseudo-code implementation, and formally establish its solid theoretical guarantees. To measure the efficacy of MementoHash, we compare its performance, in terms of memory usage and lookup time, to that of state-of-the-art algorithms, namely, AnchorHash, DxHash, and JumpHash. Unlike JumpHash, MementoHash can handle random failures. Moreover, MementoHash does not require fixing the overall capacity of the cluster (as AnchorHash and DxHash do), allowing it to scale indefinitely. The number of removed nodes affects the performance of all the considered algorithms. Therefore, we conduct experiments considering three different scenarios: stable (no removed nodes), one-shot removals (90% of the nodes removed at once), and incremental removals. We report experimental results that averaged a varying number of nodes from ten to one million. Results indicate that our algorithm shows optimal lookup performance and minimal memory usage in its best-case scenario. It behaves better than AnchorHash and DxHash in its average-case scenario and at least as well as those two algorithms in its worst-case scenario.

关键词： Clustering algorithms Peer-to-peer computing Memory management distributed databases Measurement Arrays Hash functions Consistent hashing load balancing scalability

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Impact of "An Applicable Family of Data Flow Testing Criteria"

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2025年第3期51卷 737-740页

作者： Weyuker, Elaine J. Univ Cent Florida Coll Engn & Comp Sci Orlando FL 32816 USA

We investigate the impact that the paper "An applicable family of data flow testing criteria" has had since it was published in 1988. We contrast its impact with that of an earlier paper, "Selecting software test data using data flow information" which introduced the family of data flow testing criteria and a new formal way of comparing testing criteria. We conclude that while the 1988 paper undoubtedly had a significant impact on the software testing research and practitioner communities, the earlier "Selecting software test data using data flow information" paper had significantly greater impact.

关键词： Testing Software Codes Computer languages Syntactics Standards Software testing distributed databases Upper bound Software engineering software validation software testing data flow testing

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A Transformer Network Air Temperature and Humidity Inversion Method Based on ATMS Brightness Temperature Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Xiao, Chengwang Dong, Jian Dou, Haofeng Li, Yinan Wang, Wenjing Ren, Fengchao Cent South Univ Sch Elect Informat Changsha 410083 Peoples R China China Acad Space Technol Xian Xian 710100 Peoples R China

Accurately measuring and inverting air parameters, such as air temperature and humidity, is crucial for weather forecasting, climate research, and environmental monitoring. In this letter, we propose an inversion method based on the transformer model to accurately estimate the spatial distribution of air temperature and humidity. Compared with traditional methods, the transformer model demonstrates superior ability in capturing nonlinear relationships and spatial dependencies in observational data, thereby improving inversion accuracy. Experiments conducted on real observational data have shown that compared to traditional techniques, the proposed method achieves a reduction of over 4.8% in the root mean square error (RMSE) of air temperature and over 14.2% in humidity estimation, demonstrating its high accuracy and reliability in inverting air temperature and humidity. This method provides a new approach for advancing air parameter inversion technology.

关键词： Humidity Temperature distribution Transformers Atmospheric modeling Brightness temperature distributed databases Data models Tropical cyclones Temperature measurement Temperature dependence Air temperature and humidity inversion method transformer

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Toward Autonomous distributed Clustering

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 2065-2072页

作者： Yang, Jie Lin, Chin-Teng Univ Technol Sydney Australian AI Inst GrapheneX UTS HAI Ctr Computat Intelligence & Brain Comp Interface LabS Sydney Australia

Compared to traditional centralized clustering, distributed clustering offers the advantage of parallel processing of data from different sites, enhancing the efficiency of clustering while preserving the privacy of the data at each site. However, most existing distributed clustering techniques require manual tuning of several parameters or hyperparameters, which can pose challenges for practical applications. This paper introduces a novel parameter-free distributed clustering framework known as distributed torque clustering (DTC). When dealing with data or subdata distributed across various sites, DTC predominantly executes two steps. The first step is a data reduction at each site using torque clustering, and the second step involves performing global clustering with weighted torque clustering. We compare DTC against six state-of-the-art distributed clustering algorithms and automatic centralized clustering techniques on ten large-scale or medium-scale datasets. The results show that the average rank of DTC is at least three times better than those of the other algorithms across all the datasets. Additionally, DTC can accurately predict the ground-truth number of clusters in nine out of ten datasets, further demonstrating its competitive performance and practical potential.

关键词： Clustering algorithms Torque Clustering methods Heuristic algorithms Complexity theory Partitioning algorithms distributed databases Clustering distributed clustering parameter-free autonomous unsupervised learning

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