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On Lengths of Singleton-Optimal Locally Repairable Codes

IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第1期72卷 3-12页

作者： Liu, Shu Wu, Ting-Yi Xing, Chaoping Yuan, Chen Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China Huawei Technol Co Ltd Cent Res Inst Theory Lab Labs 2012 Hong Kong 999077 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

A locally repairable code is called Singleton-optimal if it achieves the Singleton-type bound. Such codes are of great theoretic interest in the study of locally repairable codes. One of the major problems in this topic is to determine the maximum length of a q -ary Singleton-optimal locally repairable code with fixed locality and minimum distance. Unlike classical MDS codes, the maximum length of Singleton-optimal locally repairable codes is very sensitive to the minimum distance and locality. Thus, determining the maximum length of the Singleton-optimal locally repairable codes is more challenging and complicated. In literature, many efforts are paid to solve this problem especially for small distance and locality regime. Moreover, most of works also requires that (r+1)|n and the recovery sets are disjoint so as to simplify the argument, where r is locality and n is the code length. In this paper, we derive some upper bounds on the maximum length of Singleton-optimal locally repairable codes with minimum distance 5,6 and 7 without the constraint that (r+1)|n and the recovery sets are *** turns out that even without this constraint we still obtain better upper bounds for codes with small locality and distance compared to known results. Furthermore, based on our upper bounds for codes with small distance and locality, we propose the propagation rule to derive some upper bounds for codes with relatively large distance and locality assuming that (r+1)|n and recovery sets are disjoint.

关键词： Codes Upper bound Symbols Reed-Solomon codes Encoding distributed databases Codecs Locally repairable codes singleton-optimal upper bounds minimum distance

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Joint Compression and Multiuser Equalization for Multi-Carrier Massive MIMO Systems With Decentralized Baseband Processing

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 5708-5724页

作者： Xu, Yanqing Zhu, Lin Shi, Rui Chang, Tsung-Hui Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China

The decentralized baseband processing (DBP) architecture is recently proposed for massive MIMO systems to reduce the interconnection cost of fronthaul links and baseband (BB) computational complexity. This paper studies the uplink multiuser equalization (MUE) problem under the DBP architecture in a multi-carrier system. Specifically, we consider a linear compression-based MUE (LC-MUE) scheme where the distributed BB units first compress the received multi-carrier signals in the frequency domain and send dimension-reduced signals to a central unit for data equalization, leading to a multi-carrier joint compression and data equalization (MC-JCDE) design problem. The MC-JCDE problem is challenging to handle because in practice the compressor is shared across multiple subcarriers, which couples the subcarrier-wise equalizers and leads to a large-dimensional problem. To develop low-complexity algorithms, we propose two new algorithms. Specifically, the first algorithm is devised based on the block coordinated descent method and non-convex alternating direction method of multipliers, which can achieve a compelling equalization accuracy and meanwhile benefit a guaranteed convergence property. The second algorithm is heuristic but enjoys further reduced complexity, which first adopts the simple carrier-wise JCDE solution, followed by a succinct aggregation step to generate a high-quality shared compressor. Simulations show that our LC-MUE scheme and proposed algorithms can approach the centralized scheme but with notably reduced fronthaul cost.

关键词： Learning systems Costs Baseband Signal processing algorithms distributed databases Massive MIMO Computer architecture White noise Computational complexity Uplink decentralized baseband processing linear compression multiuser equalization

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KLNK: Expanding Page Boundaries in a distributed Shared Memory System

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2024年第9期35卷 1524-1535页

作者： Ci, Yi-Wei Lyu, Michael R. Zhang, Zhan Zuo, De-Cheng Yang, Xiao-Zong Chinese Acad Sci Inst Software Beijing 100045 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

Software-based distributed shared memory (DSM) allows multiple processes to access shared data without the need for specialized hardware. However, this flexibility comes at a significant cost due to the need for data synchronization. One approach to mitigate these costs is to relax the consistency model, which can lead to delayed updates to the shared data. This approach typically requires the use of explicit synchronization primitives to regulate access to the shared memory and determine the timing of data synchronization. To circumvent the need for explicit synchronization, an alternative approach is to manage shared memory transparently using the underlying system. While this can simplify programming, it often imposes a fixed granularity for data sharing, which can limit the expansion of the coherence domain and increase the synchronization requirements. To overcome this limitation, we propose an abstraction called the elastic coherence domain, which dynamically adjusts the scope of data synchronization and is supported by the underlying system for transparent management of shared memory. The experimental results show that this approach can improve the efficiency of memory sharing in distributed environments.

关键词： Synchronization Coherence distributed databases Memory management Operating systems Protocols Heuristic algorithms Operating system inter-process communication distributed shared memory

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FedVeca: Federated Vectorized Averaging on Non-IID Data With Adaptive Bi-Directional Global Objective

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2024年第11期35卷 2102-2113页

作者： Luo, Ping Cheng, Jieren Xiong, N. Liu, Zhenhao Wu, Jie Hainan Blockchain Technol Engn Res Ctr Haikou 570228 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

Federated Learning (FL) is a distributed machine learning framework in parallel and distributed systems. However, the systems' Non-Independent and Identically distributed (Non-IID) data negatively affect the communication efficiency, since clients with different datasets may cause significant gaps to the local gradients in each communication round. In this article, we propose a Federated Vectorized Averaging (FedVeca) method to optimize the FL communication system on Non-IID data. Specifically, we set a novel objective for the global model which is related to the local gradients. The local gradient is defined as a bi-directional vector with step size and direction, where the step size is the number of local updates and the direction is divided into positive and negative according to our definition. In FedVeca, the direction is influenced by the step size, thus we average the bi-directional vectors to reduce the effect of different step sizes. Then, we theoretically analyze the relationship between the step sizes and the global objective, and obtain upper bounds on the step sizes per communication round. Based on the upper bounds, we design an algorithm for the server and the client to adaptively adjusts the step sizes that make the objective close to the optimum. Finally, we conduct experiments on different datasets, models and scenarios by building a prototype system, and the experimental results demonstrate the effectiveness and efficiency of the FedVeca method.

关键词： Data models Adaptation models Vectors Training distributed databases Bidirectional control Servers Federated learning machine learning non-IID data optimization

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distributed Rumor Source Detection via Boosted Federated Learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 5986-6001页

作者： Wang, Ranran Zhang, Yin Wan, Wenchao Chen, Min Guizani, Mohsen Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610056 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510640 Peoples R China Pazhou Lab Guangzhou 510640 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Abu Dhabi 99000 U Arab Emirates

How to localize the rumor source is an extremely important matter for all sectors of the society. Many researchers have tried to use deep-learning-based graph models to detect rumor sources, but they have neglected how to train their deep-learning-based graph models in the noisy social network environment efficiently. Especially for deep learning models, the performance relies on the data scale. However, even though it is known that a substantial amount of rumor data distributed across multiple edge servers (e.g., cross-platform), due to conflicting business interests, its challenging to coordinate all parties to train a model driven by many samples while avoiding moving data. Federated learning, is an effective technique to bridge this gap. Therefore, this paper proposes a distributed Rumor Source Detection via Boosted Federated Learning (DRSDBFL). Specifically, this paper proposes an effective rumor source detection method based on a deep-learning-based graph model with a denoising module. To the best of our knowledge, we are the first to attempt the use of a denoising module to reduce the noisy effects of social networks. Then, we propose a novel boosted federated learning mechanism through boosting the high-quality edge worker to improve the training efficiency. Finally, the effectiveness of the proposed method is verified by extensive experiments.

关键词： Federated learning Social networking (online) Data models Training distributed databases Computational modeling Adaptation models Rumor source detection federated learning graph distributed

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A Unit Protection Scheme Based on the Transient Current Coordinate Mapping Interval

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PROTECTION AND CONTROL OF MODERN POWER SYSTEMS 2025年第1期10卷 103-120页

作者： Sun, Yao Fan, Yanfang Hou, Junjie Song, Guobing Xinjiang Univ Sch Elect Engn Urumqi 830000 Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710000 Peoples R China

Modular multilevel converters (MMCs) have limited ability to withstand overcurrent. Additionally, the complex characteristics of faults make it extremely difficult to reliably identify the fault area within a short period. In this paper, the waveform of the transient current component is transformed into multiple intersecting curves in an alternative coordinate system, utilizing the principle of coordinate mapping. The internal and external faults are identified by determining the range of angles between the current waveforms and the time axis based on the intersecting regions of the curves. Subsequently, a unit protection scheme based on the transient current coordinate mapping interval is proposed. Finally, a modular multi-level converter-high voltage direct current (MMC-HVDC) system is built using PSCAD/EMTDC to validate the proposed scheme. The simulation results show that the proposed protection scheme is insensitive to factors such as current fluctuations caused by noise and distributed capacitive currents. In addition, it shows high robustness against fault resistance.

关键词： Resistance Fault diagnosis Simulation distributed databases Data models Robustness Real-time systems Synchronization Protection Transient analysis Unit protection MMC-HVDC coordinate mapping transient current waveform

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Differentially Private Federated Learning With Importance Client Sampling

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3635-3649页

作者： Chen, Lin Ding, Xiaofeng Li, Mengqi Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Lab Serv Comp Technol & Syst Lab Cluster & Grid Comp Lab Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China

As numerous consumer electronics applications like smartphones and wearables generate lots of distributed data daily, consumers desire to safely and efficiently tackle private and isolated data. Federated learning (FL) is hopeful to satisfy the above requirement due to strong data security and applicability to large-scale scenarios. But diverse clients inevitably cause non-independent and identically distributed (non-iid) data among clients, which severely hinders performance analysis. Besides, affected by non-iid data, the participating clients are typically heterogeneous, which induces the client sampling problem. More importantly, albeit FL can enhance privacy via data localization, for highly secret data like physiological data from wearables, FL should possess stronger security to prevent third-party attacks. For data heterogeneity, client sampling, and privacy security, we propose differential privacy (DP) enabled and importance-aware FL algorithm DPFLICS to jointly handle these problems. Specifically, we utilize the truncated concentrated DP to tightly track the end-to-end privacy loss. To attain better sampling, the server selects partial clients with the probability derived from our importance client sampling. Moreover, to further improve performance, we also leverage the adaptive YOGI optimizer on the server side, which is an adaptive gradient method improved from the widely-used ADAM optimization. Finally, the multiple experiments exhibit the effectiveness of our method.

关键词： Servers distributed databases Consumer electronics Privacy Convergence Data models Federated learning federated learning differential privacy client sampling adaptive optimization

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Aligning Before Aggregating: Enabling Communication Efficient Cross-Domain Federated Learning via Consistent Feature Extraction

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 5880-5896页

作者： Zhu, Guogang Liu, Xuefeng Tang, Shaojie Niu, Jianwei Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Zhongguancun Lab Beijing 100081 Peoples R China Zhengzhou Univ Res Inst Ind Technol Sch Informat Engn Zhengzhou 450001 Henan Peoples R China Univ Texas Dallas Jindal Sch Management Richardson TX 75080 USA

Cross-domain federated learning (FL), where data on local clients come from different domains, is a common case of FL. In such a cross-domain case, features extracted from the raw data of different clients deviate from each other in the feature space, leading to a so-called feature shift. This phenomenon can reduce feature discrimination and degrade the performance of the learned model. However, most existing FL methods are not specifically designed for the cross-domain setting. In this article, we propose a novel cross-domain FL method named AlignFed. In AlignFed, each client model consists of a personalized feature extractor and a shared lightweight classifier. The feature extractor maps the features to a consistent space by aligning them to identical global target points. Inspired by recent studies in contrastive learning, AlignFed regards points that are uniformly distributed on the hypersphere as global target points. It then pushes features toward global target points of their corresponding classes and away from those of other classes to improve feature discrimination. The shared classifier aggregates knowledge across clients over the consistent feature space, which can mitigate performance degradation caused by feature shift while reducing communication cost. We conduct convergence analysis and perform extensive experiments to evaluate AlignFed.

关键词： Feature extraction Costs Mobile computing Degradation Training distributed databases Privacy breach Communication cost cross-domain feature alignment federated learning

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Federated and Asynchronized Learning for Autonomous and Intelligent Things

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IEEE NETWORK 2024年第2期38卷 286-293页

作者： You, Linlin Liu, Sheng Zuo, Bingran Yuen, Chau Niyato, Dusit Poor, H. Vincent Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China MIT Intelligent Transportat Syst Lab 77 Massachusetts Ave Cambridge MA 02139 USA Nanyang Technol Univ Rehabil Res Inst Singapore Singapore 639798 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nanyang Technol Univ Sch Comp Sci & Engn Jurong West 639798 Singapore Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA

The Internet of Things (IoT) intertwined with autonomous and intelligent things (AITs) is beginning to affect many aspects of our daily lives. Along with this trend, asynchronous federated learning (AFL) is an enabler of harnessing the diverse and heterogeneous sensing and computing capabilities of AITs in a collaborative and privacy-enhancing manner. In this paper, to ease the deployment and improve the performance of AFL for AITs, FedAL (Federated and Asynchronized Learning Framework) is proposed, which can orchestrate the learning process at AITs based on customizable and reusable microservices, activate AITs with high self-information changes as AFL clients to remedy overlearning, optimize the client-server interaction to support cost-efficient model updates, and enhance the model aggregation function by applying an adaptive weight measuring both the information staleness and richness of local updates. It is seen that, compared with three baselines (i.e., FedAvg, FedAsync, and FedConD), FedAL can significantly improve the overall performance in terms of model accuracy by 2.58%, communication delay by 48.83%, and communication cost by 69.84%.

关键词： Servers Data models Computational modeling Costs Robot sensing systems distributed databases Adaptation models Internet of Things Autonomous systems Intelligent systems Federated learning

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Improving Data Locality of Tasks by Executor Allocation in Spark Computing Environment

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2024年第3期12卷 876-888页

作者： Fu, Zhongming He, Mengsi Yi, Yang Tang, Zhuo Univ South China Coll Comp Sci & Technol Hengyang 421001 Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Natl Supercomp Ctr Changsha 410082 Hunan Peoples R China

The concept of data locality is crucial for distributed systems (e.g., Spark and Hadoop) to process Big Data. Most of the existing research optimized the data locality from the aspect of task scheduling. However, as the execution container of Spark's tasks, the executor launched on different nodes can directly affect the data locality achieved by the tasks. This article tries to improve the data locality of tasks by executor allocation in Spark framework. First, because of different communication modes at stages, we separately model the communication cost of tasks for transferring input data to the executors. Then formalize an optimal executor allocation problem to minimize the total communication cost of transferring all input data. This problem is proven to be NP-hard. Finally, we present a greed dropping heuristic algorithm to provide solution to the executor allocation problem. Our proposals are implemented in Spark-3.4.0 and its performance is evaluated through representative micro-benchmarks (i.e., WordCount, Join, Sort) and macro-benchmarks (i.e., PageRank and LDA). Extensive experiments show that the proposed executor allocation strategy can decrease the network traffic and data access time by improving the data locality during the task scheduling. Its performance benefits are particularly significant for iterative applications.

关键词： Task analysis Resource management Sparks Scheduling distributed databases Costs Scheduling algorithms Data locality distributed systems executor allocation Spark framework

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