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Cluster-based Federated Learning Framework for Intrusion Detection

Cluster-based Federated Learning Framework for Intrusion Det...

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international symposium on parallel Architectures, Algorithms and Programming (PAAP)

作者： Luxin Cai Naiyue Chen Yuanmeng Wei Huaping Chen Yidong Li School of Computer and Information Technology Beijing Jiaotong University Beijing China Qi’anxin Technology Group Co. Ltd.. Beijing China

ISBN: (纸本)9781665452199

With the rapid development of Industrial Internet, the network intrusion detection has become particularly important. In the Industrial Internet, large-scale data is distributed in the edge nodes caused the joint analysis of network intrusion detection at each edge node has become necessary. Federated learning structure can avoid data out of local nodes to protect user privacy data. However, the data distribution is different for each edge nodes, which limits the effectiveness of federated learning models. We focus on the non-IID data features and propose a new cluster-based federated learning framework for network intrusion detection. In this method, we cluster clients into different communities by data labels, which the clients contain the similar proportion of data labels in the same community. Based on the clustering results, we decompose federated learning model aggregation into cluster aggregation and global aggregation by leveraging similarities both within and between clusters. We conduct extensive experiments based on UNSW_NB15 dataset. The results show that our method has better performance than FedAvg and FedProx. It can work well in scenarios with different distributions of data samples while ensuring data security and privacy protection.

关键词： Data privacy Federated learning Image edge detection Data security Network intrusion detection distributed databases Programming

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ASYNC: A Cloud Engine with Asynchrony and History for distributed Machine Learning 34

ASYNC: A Cloud Engine with Asynchrony and History for Distri...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Soori, Saeed Can, Bugra Gurbuzbalaban, Mert Dehnavi, Maryam Mehri Univ Toronto Dept Comp Sci Toronto ON Canada Rutgers State Univ Dept MSIS New Brunswick NJ USA

ISBN: (纸本)9781728168760

ASYNC is a framework that supports the implementation of asynchrony and history for optimization methods on distributed computing platforms. The popularity of asynchronous optimization methods has increased in distributed machine learning. However, their applicability and practical experimentation on distributed systems are limited because current bulk-processing cloud engines do not provide a robust support for asynchrony and history. With introducing three main modules and bookkeeping system-specific and application parameters, ASYNC provides practitioners with a framework to implement asynchronous machine learning methods. To demonstrate ease-of-implementation in ASYNC, the synchronous and asynchronous variants of two well-known optimization methods, stochastic gradient descent and SAGA, are demonstrated in ASYNC.

关键词： Machine learning cloud computing

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Scalar Vector Runahead

Scalar Vector Runahead

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IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Jaime Roelandts Ajeya Naithani Sam Ainsworth Timothy M. Jones Lieven Eeckhout Ghent University Belgium University of Edinburgh UK University of Cambridge UK

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Modern graph and database processing typically takes place on high-end servers in data centers. However, with growing concerns of data privacy, trustworthiness, and all-time connectivity, there has been a shift toward increased analytics processing on edge devices such as mobile phones. In an ideal scenario, we would run these applications on the energy-efficient in-order cores available in these systems rather than the power-hungry out-of-order cores. However, these applications typically feature an extremely low computation-to-communication ratio and irregular memory accesses, meaning their performance is memory-bound, and out-of-order cores provide significant performance advantages over their in-order counterparts. Although prior work on Vector Runahead has substantially improved the performance of graph applications on very large out-of-order cores, it incurs high complexity and power consumption, so is unsuitable for energy-efficient in-order processors. Scalar Vector Runahead (SVR) extracts high memory-level parallelism on simple in-order cores by piggybacking on existing instructions executed on the processor leading to future irregular memory accesses. SVR executes multiple transient, independent, parallel instances of memory accesses and their chains initiated from different values of a predicted induction variable to move mutually independent memory accesses next to each other to hide dependent stalls. With a hardware overhead of only 2 KiB, SVR delivers $\mathbf{3.2}\times$ higher performance than a baseline 3-wide in-order core, and $\mathbf{1.3}\times$ higher performance than a full out-of-order core, while halving energy consumption. Increasing the overhead to 9 KiB to account for a larger register file, SVR can extend the speedup relative to an out-of-order core to $\mathbf{1.7}\times$ .

关键词： Out of order Microarchitecture databases Prefetching parallel processing Vectors Energy efficiency Registers Servers Transient analysis

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Synchronous parallel multisplitting method with convergence acceleration using a local Krylov-based minimization for solving linear systems

Synchronous parallel multisplitting method with convergence ...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Mé dane A. Tchakorom Raphaë l Couturier Jean-Claude Charr FEMTO-ST Institute CNRS Univ. Bourgogne Franche-Comte (UBFC) Belfort France

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Computer simulations of physical phenomena, such as heat transfer, often require the solution of linear equations. These linear equations occur in the form Ax $=\mathbf{b}$ , where A is a matrix, $\mathbf{b}$ is a vector, and $\mathbf{x}$ is the vector of unknowns. Iterative methods are the most adapted to solve large linear systems because they can be easily parallelized. This paper presents a variant of the multisplitting iterative method with convergence acceleration using the Krylov-based minimization method. This paper particularly focuses on improving the convergence speed of the method with an implementation based on the PETSc (Portable Extensible Toolkit for Scientific Computation) library. This was achieved by reducing the need for synchronization - data exchange - during the minimization process and adding a preconditioner before the multisplitting method. All experiments were performed either over one or two sites of the Grid5000 platform and up to 128 cores were used. The results for solving a 2D Laplacian problem of size 1024 2 components, show a speed up of up to 23X and 86X when respectively compared to the algorithm in [8] and to the general multisplitting implementation.

关键词： Linear systems distributed processing Minimization methods Laplace equations Mathematical models Libraries Iterative methods

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Machine Learning Aided Hardware Resource Estimation for FPGA DNN Implementations

Machine Learning Aided Hardware Resource Estimation for FPGA...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Dana Diaconu Lucian Petrica Michaela Blott Miriam Leeser Northeastern University Boston USA Xilinx Research Dublin Ireland

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

This paper explores methods of improving hardware resource estimation for the implementation of Deep Neural Networks(DNN) on FPGAs using machine learning algorithms. Current approaches consider the DNN and High Level Synthesis (HLS) levels. At the DNN level, most techniques are strictly analytical, and based on rough approximations and FPGA DNN implementation assumptions. The aim of this work is to facilitate design space exploration by providing more accurate resource estimates before running time consuming processes such as High Level Synthesis (HLS) or logic synthesis. We integrated the algorithms in FINN, an end-to-end framework for building Quantized Neural Networks (QNN) FPGA inference accelerators, in order to evaluate and compare them to existing estimation as well as the actual synthesized design. We generated Support Vector Regression (SVR) models for LUT and BRAM estimation, the former yields promising results, while the latter consistently underperforms in comparison to HLS and analytical FINN estimates. Combining the analytical approach used in FINN with SVR LUT estimation provided more accurate results because on its own, SVR had insufficient extrapolation capability. For BRAM estimation, we improved the analytical approach by using a Decision Tree Classifier for predicting distributed or BRAM memory implementation.

关键词： Support vector machines Extrapolation databases Estimation Random access memory Hardware Table lookup

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Use of Blockchain for Ensuring Data Integrity in Cloud databases 5th

Use of Blockchain for Ensuring Data Integrity in Cloud Datab...

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5th international symposium on Cyber Security Cryptography and Machine Learning (CSCML)

作者： Vainshtein, Yakov Gudes, Ehud Open Univ Dept Math & Comp Sci Raanana Israel Ben Gurion Univ Negev Dept Comp Sci Beer Sheva Israel

ISBN: (纸本)9783030780869;9783030780852

This paper proposes a novel method for using a PoW-based Blockchain to ensure data integrity in cloud database management systems. The use of cloud platforms for storing data or even hosting databases is incredibly huge, and in many cases, there is no convenient way for a client to check the integrity of the data stored in the cloud database. To solve this, we propose a technique based on an interaction between the cloud platform and a PoW-based Blockchain. This interaction exploits a distributed Hash Table and lightweight software agents, which are monitoring changes done to cloud database storage nodes. Data update operations are published by the agents as Blockchain log/audit transactions that propagate deep into the Blockchain network until they become immutably and cryptographically protected by it. The proposed method enables the Cloud Provider to manage metadata so that it will be able to easily detect deliberate or accidental corruptions of transactions and to recover the transactions in case such a data corruption incident occurs.

关键词： PoW-based Blockchain Data integrity Cloud database

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Evaluating the Potential of In-Memory Processing to Accelerate Homomorphic Encryption: Practical Experience Report

Evaluating the Potential of In-Memory Processing to Accelera...

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Reliable distributed systems

作者： Mpoki Mwaisela Joel Hari Peterson Yuhala Jämes Ménétrey Pascal Felber Valerio Schiavoni University of Neuchâtel Switzerland University of Bern Switzerland

ISBN: (数字)9798331530037

ISBN: (纸本)9798331530044

The widespread adoption of cloud-based solutions introduces privacy and security concerns. Techniques such as homomorphic encryption (HE) mitigate this problem by allowing computation over encrypted data without the need for decryption. However, the high computational and memory overhead associated with the underlying cryptographic operations has hindered the practicality of HE-based solutions. While a significant amount of research has focused on reducing computational overhead by utilizing hardware accelerators like GPUs and FPGAs, there has been relatively little emphasis on addressing HE memory overhead. Processing in-memory (PIM) presents a promising solution to this problem by bringing computation closer to data, thereby reducing the overhead resulting from processor-memory data movements. In this work, we evaluate the potential of a PIM architecture from UPMEM for accelerating HE operations. Firstly, we focus on PIM-based acceleration for polynomial operations, which underpin HE algorithms. Subsequently, we conduct a case study analysis by integrating PIM into two popular and open-source HE libraries, OpenFHE and HElib. Our study concludes with key findings and takeaways gained from the practical application of HE operations using PIM, providing valuable insights for those interested in adopting this technology.

关键词： Privacy parallel processing Data transfer In-memory computing Polynomials Libraries Cryptography Reliability Homomorphic encryption Hardware acceleration

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Research and Development of Enterprise Data Warehouse Based on SAP BW Modeling

Research and Development of Enterprise Data Warehouse Based ...

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Smart Information systems and Technologies (SIST), IEEE international Conference on

作者： Gulzat Turken Lyazat Naizabayeva Maxatbek Satymbekov Zukhra Abdiakhmetova Department of Computer Science Al-Farabi Kazakh National University Almaty Kazakhstan Department of Information System International Information Technology University Almaty Kazakhstan

The data warehouse is based on the development of relational databases, parallel processing and distributed processing technologies, as well as online analytical processing technologies. SAP BW is widely used in many enterprise data warehouses. It can issue reports of enterprises at the group level, manage the group's business operations, help find the laws and trends behind the performance of corporations and ultimately provide decision-making support for decision makers of enterprises. Therefore, how to use the SAP BW model architecture to better realize enterprise data warehouses is one of the hot topics in the data warehouse industry today. For a large corporation, its branches and subsidiaries have deployed multiple sets of enterprise data warehouses. At the same time, because there are multiple sets of data warehouse systems, when the data model needs to be modified at the group level, the data model of the branch also needs to be modified accordingly, and the flexibility of maintenance is poor. Finally, every time the corporation needs to upgrade the version of the data warehouse, it needs to maintain and upgrade the version of the data warehouse of each branch and the maintenance cost is high. Therefore, how to integrate multiple sets of data warehouses distributed in various subsidiaries into a corporation enterprise data warehouse by designing the model hierarchy, so as to ensure the reliability of data, enhance the flexibility of model modification and reduce the cost of data warehouse. This paper completes the design of an enterprise data warehouse of a large corporation, which can be used for the summary and collation of data from multiple branches such as finance and sales of a corporation, effectively improving the efficiency of data utilization and ensuring the authenticity of data. At the same time, through hierarchical design, the problems of poor flexibility in model modification and high system maintenance costs are solved.

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Dalorex: A Data-Local Program Execution and Architecture for Memory-bound Applications

Dalorex: A Data-Local Program Execution and Architecture for...

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IEEE symposium on High-Performance Computer Architecture

作者： Marcelo Orenes-Vera Esin Tureci David Wentzlaff Margaret Martonosi Princeton University Princeton New Jersey USA

Applications with low data reuse and frequent irregular memory accesses, such as graph or sparse linear algebra workloads, fail to scale well due to memory bottlenecks and poor core utilization. While prior work with prefetching, decoupling, or pipelining can mitigate memory latency and improve core utilization, memory bottlenecks persist due to limited off-chip bandwidth. Approaches doing processing in-memory (PIM) with Hybrid Memory Cube (HMC) overcome bandwidth limitations but fail to achieve high core utilization due to poor task scheduling and synchronization overheads. Moreover, the high memory-per-core ratio available with HMC limits strong *** introduce Dalorex, a hardware-software co-design that achieves high parallelism and energy efficiency, demonstrating strong scaling with >16,000 cores when processing graph and sparse linear algebra workloads. Over the prior work in PIM, both using 256 cores, Dalorex improves performance and energy consumption by two orders of magnitude through (1) a tile-based distributed-memory architecture where each processing tile holds an equal amount of data, and all memory operations are local; (2) a task-based parallel programming model where tasks are executed by the processing unit that is co-located with the target data; (3) a network design optimized for irregular traffic, where all communication is one-way, and messages do not contain routing metadata; (4) novel traffic-aware task scheduling hardware that maintains high core utilization; and (5) a data-placement strategy that improves work *** work proposes architectural and software innovations to provide the greatest scalability to date for running graph algorithms while still being programmable for other domains.

关键词： Technological innovation Software algorithms distributed databases Linear algebra Bandwidth Energy efficiency Software

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distributed Dense Tucker Decomposition Based on Hierarchical SVD 11th

Distributed Dense Tucker Decomposition Based on Hierarchical...

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11th international symposium on parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Fang, Zisen Qi, Fumin Dong, Yichuan Zhang, Yong Feng, Shengzhong National Supercomputing Center in Shenzhen Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Science Beijing China

ISBN: (纸本)9789811600098

As an important tool of multi-way/tensor data analysis tool, Tucker decomposition has been applied widely in various fields. But traditional sequential Tucker algorithms have been outdated because tensor data is growing rapidly in term of size. To address this problem, in this paper, we focus on parallel Tucker decomposition of dense tensors on distributed-memory systems. The proposed method uses Hierarchical SVD to accelerate the SVD step in traditional sequential algorithms, which usually takes up most computation time. The data distribution strategy is designed to follow the implementation of Hierarchical SVD. We also find that compared with the state-of-the-art method, the proposed method has lower communication cost in large-scale parallel cases under the assumption of the α–β model. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Tensors

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