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Dynamic Clustering and Power Control for Two-Tier Over-the-Air Federated Learning

Dynamic Clustering and Power Control for Two-Tier Over-the-A...

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IEEE/CIC international conference on Communications in China (ICCC)

作者： Guo, Wei Huang, Chuan Qin, Xiaoqi Yan, Lian Zhang, Wei Chinese Univ Hong Kong Sch Sci & Engn SSE Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Future Network Intelligence Inst FNii Shenzhen 518172 Peoples R China Peng Cheng Lab Shenzhen 518006 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Univ Elect Sci & Technol China Informat & Commun Engn Coll Chengdu 611731 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

Federated learning (FL) has been recognized as a promising distributed learning paradigm to support intelligent applications at the wireless edge. However, direct computing based on the information from the devices may not be resource efficient due to the relatively large communication cost between the devices and parameter server (PS). This paper studies the joint communication and learning design for the over-the-air computation (AirComp)-based two-tier wireless FL scheme, where the lead devices first collect the local gradients from their nearby subordinate devices, and then send the merged results to the PS for the second round of aggregation. We establish a convergence result for the proposed scheme and derive the upper bound on the optimality gap between the expected and optimal global loss values. Next, based on the device distance and data importance, we propose a hierarchical clustering method to build the two-tier structure. Then, with only the instantaneous channel state information (CSI), we formulate the optimality gap minimization problem and solve it by using an efficient alternating minimization method. Numerical results show that the proposed scheme outperforms the baseline ones.

关键词： Wireless communication Minimization methods Upper bound Distance learning Clustering methods Power control Lead

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distributed Task Offloading based on Multi-Agent Deep Reinforcement Learning 17

Distributed Task Offloading based on Multi-Agent Deep Reinfo...

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17th IEEE international conference on Mobility, Sensing and networking (MSN)

作者： Hu, Shucheng Ren, Tao Niu, Jianwei Hu, Zheyuan Xing, Guoliang Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Zhengzhou Univ Res Inst Ind Technol Sch Informat Engn Zhengzhou 450001 Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781665406680

Recent years have witnessed the increasing popularity of mobile applications, e.g., virtual reality, unmanned driving, which are generally computation-intensive and latency-sensitive, posing a major challenge for resource-limited user equipment (UE). Mobile edge computing (MEC) has been proposed as a promising approach to alleviate the problem, by offloading mobile tasks to the edge server (ES) deployed in close proximity to UE. However, most existing task offloading algorithms are primarily based on centralized scheduling, which could suffer from the 'curse of dimensionality' in large MEC environments. To address this issue, this paper proposes a fully distributed task offloading approach based on multi-agent deep reinforcement learning, whose critic and actor neural networks are trained under the assistance of global and local network states, respectively. In addition, we design a model parameter aggregation mechanism, along with a normalized fine-tuned reward function, to further improve the learning efficiency of the training process. Simulation results show that our proposed approach could achieve substantial performance improvements over baseline approaches.

关键词： task offloading mobile edge computing multi-agent deep reinforcement learning

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Teaching Toy Car to Self-drive in a Game Environment using Reinforcement Learning

Teaching Toy Car to Self-drive in a Game Environment using R...

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Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Purna Jain Saumya Tiwari Saud Hashmi Gaurav Sharma Department of Computer Science Engineering Medi-Caps University Indore India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

This research paper explores the use of reinforcement learning (RL) to teach a toy car to self-drive in a game environment. The study focuses on the development of an RL-based algorithm that enables the toy car to learn the optimal driving behavior through trial-and-error experience. The game environment provides a simulated environment for the toy car to navigate, and the RL algorithm rewards the car for taking the right actions while penalizing it for taking the wrong ones. The study also examines the effects of different reward structures and hyperparameters on the performance of the RL algorithm. The results of the study demonstrate that the toy car can learn to drive autonomously in the game environment and that the performance of the RL algorithm improves with appropriate reward structures and hyperparameters. The outcomes of this study’s conclusions may have an impact on the creation of autonomous driving systems and robotics.

关键词： Training Visualization Toy manufacturing industry Supervised learning distributed databases Stochastic processes Games

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Bio-Inspired Energy Distribution for Programmable Matter 21

Bio-Inspired Energy Distribution for Programmable Matter

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22nd international conference on distributed computing and networking (ICDCN)

作者： Daymude, Joshua J. Richa, Andrea W. Weber, Jamison W. Arizona State Univ Comp Sci CIDSE Tempe AZ 85287 USA

ISBN: (纸本)9781450389334

In systems of active programmable matter, individual modules require a constant supply of energy to participate in the system's collective behavior. These systems are often powered by an external energy source accessible by at least one module and rely on module-to-module power transfer to distribute energy throughout the system. While much effort has gone into addressing challenging aspects of power management in programmable matter hardware, algorithmic theory for programmable matter has largely ignored the impact of energy usage and distribution on algorithm feasibility and efficiency. In this work, we present an algorithm for energy distribution in the amoebot model that is loosely inspired by the growth behavior of Bacillus subtilis bacterial biofilms. These bacteria use chemical signaling to communicate their metabolic states and regulate nutrient consumption throughout the biofilm, ensuring that all bacteria receive the nutrients they need. Our algorithm similarly uses communication to inhibit energy usage when there are starving modules, enabling all modules to receive sufficient energy to meet their demands. As a supporting but independent result, we extend the amoebot model's well-established spanning forest primitive so that it self-stabilizes in the presence of crash failures. We conclude by showing how this self-stabilizing primitive can be leveraged to compose our energy distribution algorithm with existing amoebot model algorithms, effectively generalizing previous work to also consider energy constraints.

关键词： programmable matter self-organization distributed algorithms biologically-inspired algorithms biofilms energy

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An Approach to Detect Air Quality using Machine Learning and Data Analysis Tools

An Approach to Detect Air Quality using Machine Learning and...

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international conference on computing and networking Technology (ICCNT)

作者： Anirban Roy Mehnaz Rashid Mim Department of Computer Science & Engineering Daffodil International University Dhaka Bangladesh

After the headway of the computerized structure, Information science hopes to be a fundamental part here. Information science stores and investigates information of various sorts of banks, foundations, workplaces and others. The framework answers in this manner subject to learning. Precise learning gives the exact outcome in any case its produce blended forecast. Along these lines, this learning is the most significant part for computerized framework. Information science is basically expectation based. Machine can imagine result without help from some other individuals rely on the given dataset. Generally, that predicted outcome is more accurate, once in a while not. The certified guess is obligated to learning accuracy. Thusly, Information science is distributed four frameworks reliant upon learning, for example, Ostensible, Ordinal, Discrete, Ceaseless. Directed systems are reliant upon marked information. Also, unstructured dataset is utilized in unaided realizing, where model information is given as of now doesn't yield. In this work we attempted to anticipate lessen air contamination in various nations with python programming language, Google studio and RFM run application. For that we gathered a dataset from public outflows answered to the Show on Lengthy reach Transboundary Air Contamination (LRTAP Show). Air poison discharges — EEA datasets. A gigantic extent of individuals kicks the bucket purposes behind vascular disease at of air contamination. Early discovery of air contamination might help the patient defeat the infection. This might be an approach to forestalling this sickness. The Information science method is essentially usable systems in this manner. In this paper the result is the gamble variables of air toxins which is in table 4 and the aftereffect of RMSE in XG Lift calculation.

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Preparing for HPC on RISC-V: Examining Vectorization and distributed Performance of an Astrophysics Application with HPX and Kokkos

Preparing for HPC on RISC-V: Examining Vectorization and Dis...

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High Performance computing, networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Patrick Diehl Panagiotis Syskakis Gregor Daiß Steven R. Brandt Alireza Kheirkhahan Srinivas Yadav Singanaboina Dominic Marcello Chris Taylor John Leidel Hartmut Kaiser Applied Computer Science (CCS-7) Los Alamos National Laboratory Los Alamos NM U.S.A. Department of Physics and Astronomy Louisiana State University Baton Rouge LA U.S.A. LSU Center for Computation & Technology Louisiana State University Baton Rouge LA U.S.A. IPVS University of Stuttgart Stuttgart Germany Tactical Computing Labs Lindsay TX U.S.A. Department of Computer Science Louisiana State University Baton Rouge LA U.S.A.

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

In recent years, interest in RISC-V computing architectures has moved from academic to mainstream, especially in the field of High Performance computing where energy limitations are increasingly a concern. As of this year, the first single board RISC-V CPUs implementing the finalized ratified vector specification are being released. The RISC-V vector specification follows in the tradition of vector processors found in the CDC STAR-100, the Cray-1, the Convex C-Series, and the NEC SX machines and accelerators. The family of vector processors offers support for variable-length array processing as opposed to the fixed-length processing functionality offered by SIMD. Vector processors offer opportunities to perform vector-chaining which allows temporary results to be used without the need to resolve memory *** this work, we use the Octo-Tiger multi-physics, multi-scale, 3D adaptive mesh refinement astrophysics application to study these early RISC-V chips with vector machine support. We report on our experience in porting this modern C++ code (which is built upon several open-source libraries such as HPX and Kokkos) to RISC-V. In addition, we show the impact of the RISC-V Vector extension on a RISC-V single board computer by implementing the std :: experimental:simd interface and integrating it with our code. We also compare the application’s performance, scalability, and power consumption on desktop-grade RISC-V computer to an A64FX *** results presented in this paper are part of a longer-term evaluation of RISC-V’s viability for HPC applications.

关键词： Power demand Astrophysics High performance computing Europe Computer architecture Hardware Vectors Supercomputers Libraries Vector processors

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A distributed AI System for Improving Single-Domain Network Intent Resolution

A Distributed AI System for Improving Single-Domain Network ...

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international conference on Intelligence in Next Generation Networks (ICIN)

作者： Pedro Martinez-Julia Ved P. Kafle Hitoshi Asaeda Network Architecture Laboratory Network Research Institute National Institute of Information and Communications Technology Tokyo Japan

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Resolving network intents to network service descriptions (NSDs) is a key process in intent-based networking (IBN). Current state-of-the-art systems for single-domain intent resolution operate independently. Agents usually resolve the network intents of their domains without interacting with other agents. However, some aspects of the input network intents will be better interpreted and understood by other agents. We propose to use a distributed multi-agent system to enhance single-domain network intent resolution. Each agent of our proposed system delivers artificial intelligence (AI) operations through a machine-interactive interface. Agents make use of this interface to request other agents the resolution of parts of the input intents. The responses are gathered and a resulting NSD is produced. We demonstrate that adding external intelligence to the operation highly improves the network intent resolution process, reaching 100% accurate resolutions, which is more than 40% improvement over current state-of-the-art solutions.

关键词： Technological innovation Cloud computing Accuracy Artificial intelligence Multi-agent systems

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DUCSAN 2020: 1st international Workshop on distributed Ubiquitous computing: Systems, Applications, and networking - Welcome and Committees

DUCSAN 2020: 1st International Workshop on Distributed Ubiqu...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

ISBN: (数字)9781728147161

ISBN: (纸本)9781728147178

The First international workshop on distributed Ubiquitous computing: Systems, Applications, and networking (DUCSAN) aims to bridge communities that have traditionally focused on different aspects of large-scale pervasive and distributed systems. As technology and research get more and more specialized, the problem of information silo effect becomes more and more prevalent. The main focus of the workshop is to be a forum to encourage thought-provoking discussion between the PerCom community and the system and networking communities. We also hope to encourage interdisciplinary discussion and collaboration through lively and interactive activities.

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SpDISTAL: Compiling distributed Sparse Tensor Computations

SpDISTAL: Compiling Distributed Sparse Tensor Computations

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Supercomputing conference

作者： Rohan Yadav Alex Aiken Fredrik Kjolstad Stanford University Stanford CA United States

ISBN: (纸本)9781665454452

We introduce SpDISTAL, a compiler for sparse tensor algebra that targets distributed systems. SpDISTAL combines separate descriptions of tensor algebra expressions, sparse data structures, data distribution, and computation distribution. Thus, it enables distributed execution of sparse tensor algebra expressions with a wide variety of sparse data structures and data distributions. SpDISTAL is implemented as a C++ library that targets a distributed task-based runtime system and can generate code for nodes with both multi-core CPUs and multiple GPUs. SpDISTAL generates distributed code that achieves performance competitive with hand-written distributed functions for specific sparse tensor algebra expressions and that outperforms general interpretation-based systems by one to two orders of magnitude.

关键词： Tensors Codes Runtime Program processors Algebra distributed databases Programming

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Intelligent Network Edge with distributed SDN for the Future 6G Network

Intelligent Network Edge with Distributed SDN for the Future...

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IEEE international conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS)

作者： Weinstein, Stephen B. Lou, Yuan-Yao Hsing, T. Russell Commun Theory & Technol Consulting New York NY 10023 USA Purdue Univ W Lafayette IN 47907 USA Natl Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9780738146720

Sixth generation wireless communication promises extremely high data rates, exceptionally low latency, and extensive use of an intelligent fog/edge platform including storage, communications, control and processing resources. This fog/edge platform will provide scalable and elastic distributed processing, communications flow management, and application support capabilities. An extended, more distributed SDN (software defined network) is likely to be an important element of this environment, enabling local picocell clients to more efficiently share processing and communications resources. This distributed SDN, with a local SDN controller handling local needs such as handoffs between picocells with very low latency and networked with distant SDN controllers for wide-area communication and information retrieval needs, will mediate the QoS for both highly interactive local applications and large volume streaming and downloading. The distributed SDN will facilitate access to application processing and locally stored data in the fog/edge platform and keep track of networking transactions and resource use. This concept of a distributed SDN coupled with network edge computing and data storage will support the entrepreneurial development of new network modalities and applications meeting the needs of network operators, service providers, and end users. This introductory paper, including a vehicular control simulation example, suggests the possible shape of an intelligent network edge incorporating a distributed SDN for the future 6G network.

关键词： fog/edge SDN 6G distributed network control

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