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Dynamic Staleness Control for Asynchronous Federated Learning in Decentralized Topology 18th

Dynamic Staleness Control for Asynchronous Federated Learni...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Ma, Qianpiao Liu, Jianchun Jia, Qingmin Zhou, Xiaomao Hu, Yujiao Xie, Renchao Purple Mountain Laboratories Jiangsu Nanjing China School of Computer Science and Technology University of Science and Technology of China Anhui Hefei China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783031714665

Decentralized federated learning (DFL) has emerged as a promising paradigm for distributed machine learning over edge nodes (i.e., workers) without relying on a centralized parameter server. Most existing DFL researches rely on synchronous communication among workers. However, due to edge heterogeneity and dynamic network conditions, synchronous DFL mechanisms may suffer from inefficient model training and poor scalability. Meanwhile, the existing asynchronous DFL (ADFL) mechanisms present the challenge of stale models among workers, leading to diminished training quality, especially on Non-IID data. In this paper, we propose a novel staleness-aware ADFL (SA-ADFL) mechanism, aiming to realize a trade-off between model training efficiency and quality by dynamic staleness control. Specifically, we provide rigorous theoretical proof for SA-ADFL and formulate the worker scheduling problem to minimize total model training time under flexible long-term staleness constraints. then we decompose the original round-coupled problem into a series of single-round sub-problems by leveraging the Lyapunov optimization, enabling efficient worker selection to minimize training time in each round while ensuring staleness queue stability. Experimental results demonstrate that our SA-ADFL accelerates model training by approximately 52.9% while maintaining equivalent model training accuracy compared with the state-of-the-art mechanisms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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A distributed TSN Time Synchronization Algorithm with Increased Tolerance for Failure Scenarios

A Distributed TSN Time Synchronization Algorithm with Increa...

引用

international conference on Computer and Communications (ICCC)

作者： Yifei Peng Xinlin Wu Xiaodong Tu Tigang Jiang Du Xu Jun Xie School of Information and Communication Engineering University of Electronic Science & Technology of China Chengdu China

the integration of Industrial Internet of things (IIoT) and Time-Sensitive networking (TSN) enables efficient and reliable real-time communication and control. Currently, one of the current research hotspots in the field of TSN is the improvement of reliability in the use of IEEE 802.1AS(IEEE Standard for Local and metropolitan area networks - Timing and Synchronization for Time-Sensitive Applications). On one hand, IEEE 802.1AS does not provide specific steps for constructing redundant clock trees, and on the other hand, it cannot detect error time values caused by the failure propagation of devices on the clock tree. To address these issues, this paper proposes a distributed time synchronization strategy based on IEEE 802.1AS(DAS). DAS utilizes a broadcast-based message exchange, and each endpoint device calculates its own synchronized time using fault-tolerant time synchronization methods. Furthermore, the time information is no longer embedded in data packets, avoiding the impact of erroneous time values on the time synchronization of other devices. Extensive simulations and on-board verifications are conducted on the DAS algorithm in this paper. the results show that the DAS algorithm can tolerate more failure scenarios while maintaining similar time synchronization accuracy compared to IEEE 802.1AS. Although the DAS algorithm consumes more bandwidth, this overhead is acceptable in small-scale networks. As far as we know, our work is the first in the industry to simultaneously achieve compatibility with existing 802.1AS and distributed TSN time synchronization, and to physically implement it in switch.

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distributed algorithm for computation offloading in mobile edge computing 2021

Distributed algorithm for computation offloading in mobile e...

引用

9th international conference on Communications and Broadband networking, ICCBN 2021

作者： Huang, Lei Zheng, Yifeng Yang, Jingmin Zhang, Wenjie Yang, Liwei Yeo, Chai Kiat School of Computer Sciences Minnan Normal University Zhangzhou China Department of Electronic Engineering National Taipei University of Technology Taibei Taiwan College of Information and Electrical Engineering China Agricultural University Beijing China School of Computer Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781450389174

User mobility is an intrinsic trait of many MEC applications, which has posed significant challenges for realizing reliable computing. However, existing works studying this problem mainly focus on the movements of users while another high-dynamic behavior due to the randomness of computation tasks is largely ignored. To fill this gap, in this paper, we formulate the computation offloading problem in MEC system as a combinatorial optimization problem, and then we construct a Markov chain with steady-state distribution specifying to our problem in a distributed manner, such that the user mobility problem is transformed into the state transition problem. Moreover, this Markov chain is further extended to consider a dynamic scenario where the number of active users in the MEC system changes over time. Numerical results show that our proposed algorithm can converge very fast to the optimal solution, and has a provable performance with a guaranteed loss bound. © 2021 ACM.

关键词： computation offloading

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Categorizing Disaster Tweets Using Learning Based Models for Emergency Crisis Management

Categorizing Disaster Tweets Using Learning Based Models for...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： Asinthara K Meghna Jayan Lija Jacob Department of Data Science Christ University Bangalore India

Social media communication is essential to the crisis response aftermath of a massive tragedy. Facebook, Twitter, and other social media network platforms are effective instruments for connecting and fostering collaboration among catastrophe victims and other groups. As a result, numerous research publications on tweet analysis have been released. Tweet analysis during a crisis helps in understanding the nuances of the incident. Existing works primarily focused on tweet sentiment analysis and binary categorization of tweets into catastrophe relevant or not. Our work mainly categorizes catastrophe tweets into seven categories: Blizzard, earthquake, flood, hurricane, tornado, wildfire, and not-relevant tweets. Deep learning and machine learning methods were employed to categorize the tweets. the annotated data is subjected to classification using Support Vector Machine (SVM) utilizing Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer and Word2Vec Vectorizer and compares the accuracy of different kernel functions. Bidirectional Long Short Term Memory (Bi-LSTM) is used on the labeled data as a deep learning technique. SVM exhibited 88% accuracy compared to 87% for Bi-LSTM. Empirical evidence shows that our methodology is more productive and efficient than previous approaches. From this knowledge of the incident, emergency aid organizations may draw conclusions and act immediately.

关键词： Support vector machines Deep learning Sentiment analysis Social networking (online) Blogs Organizations Disaster management

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Dynamic Task Scheduling in LEO Satellites Mega-constellations: A Domain-based Perspective

Dynamic Task Scheduling in LEO Satellites Mega-constellation...

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international conference on Computer and Communications (ICCC)

作者： Zeru Fang Yan Liu Kaige Zhu Feng Sun Chenguang Shi Renchao Xie Qinqin Tang Tao Huang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Shanghai Key Laboratory of Satellite Network Shanghai Satellite Network Research Institute Co. Ltd. Shanghai China

Low Earth orbit (LEO) satellite mega-constellations have become an essential part of future networks to achieve global coverage. With the significant development of edge computing, LEO satellite mega constellations are becoming capable of on-orbit computing. To address the constraints imposed by limited computing resources, satellite cooperative task scheduling has become a promising paradigm. However, most of the existing studies focus on small-scale task scheduling between neighboring nodes. they do not fully reflect the large-scale characteristics of mega constellations. therefore, we consider a computational domain-based architecture. Based on this architecture, we investigate a scheme for solving large-scale task scheduling in LEO satellites mega-constellations, which enables intra- and inter-domain task scheduling optimization. Specifically, we use a reinforcement learning-based approach to obtain the lowest task processing cost in intra-domain scheduling. For inter-domain task scheduling, we balance the load of each computational domain from a global perspective. then, considering the strong correlation and continuity between intra-domain scheduling and inter-domain scheduling, we propose the SAC-GALB algorithm. Numerical results verify the effectiveness and superiority of the scheme.

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A Malicious Domain Detection Method of Cryptomining Based on Deep Learning

A Malicious Domain Detection Method of Cryptomining Based on...

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international conference on Computer and Communications (ICCC)

作者： Wei Zheng Xuange Huang Renchao Xie Qinqin Tang Tao Huang China Academy of Information and Communications Technology Beijing China Jiangsu Future Network Innovation Institute Nanjing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

With the skyrocketing prices and soaring trading volumes of Bitcoin and other cryptocurrencies, the harms caused by malicious cryptomining activities are also increasing. Hackers are increasingly utilizing malicious software to conduct network attacks for cryptocurrency mining, posing threats not only to user privacy but also leading to the consumption of computing resources and increased electricity costs. Despite these challenges, existing detection methods, such as using blacklists to protect users’ browser antivirus programs, only offer partial solutions to this problem, as attackers can easily bypass their detection by frequently changing their domain names using domain generation algorithms. To address these issues, this paper employs deep learning technology and designs a method for detecting malicious cryptomining domains. this method combines blacklist detection with Long Short-Term Memory (LSTM) and is capable of identifying malicious domains from a large number of domain samples. Experimental results demonstrate that the proposed method produces excellent classification and detection outcomes.

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PRACTICAL MASSIVELY PARALLEL MONTE-CARLO TREE SEARCH APPLIED TO MOLECULAR DESIGN 9

PRACTICAL MASSIVELY PARALLEL MONTE-CARLO TREE SEARCH APPLIED...

引用

9th international conference on Learning Representations, ICLR 2021

作者： Yang, Xiufeng Aasawat, Tanuj Kr Yoshizoe, Kazuki Chugai Pharmaceutical Co. Ltd Japan Parallel Computing Lab - India Intel Labs India RIKEN Center for Advanced Intelligence Project Japan

It is common practice to use large computational resources to train neural networks, known from many examples, such as reinforcement learning applications. However, while massively parallel computing is often used for training models, it is rarely used to search solutions for combinatorial optimization problems. this paper proposes a novel massively parallel Monte-Carlo Tree Search (MP-MCTS) algorithm that works efficiently for a 1,000 worker scale on a distributed memory environment using multiple compute nodes and applies it to molecular design. this paper is the first work that applies distributed MCTS to a real-world and non-game problem. Existing works on large-scale parallel MCTS show efficient scalability in terms of the number of rollouts up to 100 workers. Still, they suffer from the degradation in the quality of the solutions. MP-MCTS maintains the search quality at a larger scale. By running MP-MCTS on 256 CPU cores for only 10 minutes, we obtained candidate molecules with similar scores to non-parallel MCTS running for 42 hours. Moreover, our results based on parallel MCTS (combined with a simple RNN model) significantly outperform existing state-of-the-art work. Our method is generic and is expected to speed up other applications of MCTS. © 2021 ICLR 2021 - 9th international conference on Learning Representations. All rights reserved.

关键词： Reinforcement learning

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Design and Development of a Log Management System Based on Cloud Native Architecture

Design and Development of a Log Management System Based on C...

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international conference on Systems and Informatics (ICSAI)

作者： Yuchen Sun Yanpiao Chen Haotian Zhao Shan Peng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

With the rapidly development and wide application of cloud computing and service computing, the use of cloud native architecture has become a mainstream trend in software development and deployment. In this booming background, logs have become the key information for each application to record its operation process throughout its life cycle, while existing log management systems are usually designed for specific production environments, which are not able to effectively meet the growing and diversified scenarios. the existing log management systems are usually designed for specific production environments and cannot effectively meet the needs of the growing number of diverse scenarios. therefore, how to effectively handle log information from multiple sources becomes a key issue. In this paper, we design and develop a log management platform based on cloud-native architecture, which provides log collection, transmission, storage, and system management functions, and at the same time can be oriented to the cloud-native environment and other third-party applications for collection, providing a powerful tool for developers and operation and maintenance personnel. In addition, we designed an anomaly detection system using large language models for log parsing and used this system for anomaly detection in platform logs. Finally, the effectiveness of the system was verified by commonly used log data. the log management platform based on cloud-native architecture passed the test and has been utilized in our real production.

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A comparative study of two different optimization methods applied to the static job scheduling problem in grid computing

A comparative study of two different optimization methods ap...

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international conference on Computer and Communications (ICCC)

作者： Rgis Donald Hontinfinde Ariel Kamoyedji Marcos thyrbus Vitouley Roland Smako Honfo Laboratory of Science Engineering and Mathematics University of Abomey (UNSTIM) Abomey Republic of Benin Department of Information and Communication Systems Okayama University Okayama Japan

In the telecommunications sector where real-time data processing and low latency are essential, grid computing promotes faster response times and reduced network congestion. Grid computing has since become a very powerful and indispensable tool for telecommunications operators because it allows companies to optimize their operations, improve the performance of their network and provide better services to their customers. One of the distributed computing systems used in telecommunications is the User-PC (UPC) system. the User-PC computing (UPC) system provides a cost-effective distributed computing platform by utilizing the unused resources of personal computers (PCs). Effective job allocation methods are essential for optimizing system performance. In this study, we introduce a comparative analysis of the static job scheduling problem in the UPC system, focusing on the performance comparison between our proposed hybrid approach, which combines a greedy algorithm and a algorithm genetics, and the approach based on Randomized Multi-Start Local Search. Our hybrid algorithm is composed of two steps. In the first step, a Greedy Algorithm is used to create an initial population of candidate solutions by assigning jobs to available workers based on CPU core utilization. In the second step, a Genetic Algorithm is used to optimize job-worker assignments through selection, crossover and mutation operations. To evaluate the performance of our approach, extensive experiments were conducted with up to 27 jobs and 6 worker PCs. the results show that our hybrid approach achieved a shorter makespan compared to the approach based on Randomized Multi-Start Local Search. Specifically, our algorithm outperformed the multi-start random local search-based method by more than 31% in terms of makespan reduction. the experimental results highlight the effectiveness of our hybrid approach to improve job scheduling performance in the UPC system.

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MANTRA: an Edge-computing Framework based on Multi-Armed Bandit for Latency- and Energy-aware Job Offloading in Vehicular Networks

MANTRA: an Edge-Computing Framework based on Multi-Armed Ban...

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IEEE conference on Network Softwarization (NetSoft)

作者： Fabio Busacca Sergio Palazzo Raoul Raftopoulos Giovanni Schembra CNIT Research Unit University of Catania Catania Italy

the optimization of job offloading procedures in modern vehicular networks is a problem of utmost importance. In this regard, this paper proposes MANTRA, a distributed framework based on multi-player multi-armed bandit (MP-MAB) algorithms for latency- and energy-aware job offloading in vehicular networks. the main goal of MANTRA is to support procedures of job offloading in green vehicular networks to achieve a target tradeoff between energy consumption and job processing latency. In particular, MANTRA is intended to run on so-called MEC-in-a-box (M-Box) devices, portable battery-powered Road Side Units (RSUs) specifically designed to work without mobile connectivity and of a fixed power *** demonstrate MANTRA effectiveness, we model the vehicular network using the queueing theory for M/M/m/K systems. We run an extensive evaluation campaign and compare MANTRA with several baselines, including a centralized, oracle-based approach. In such a way, we demonstrate how MANTRA outperforms the baselines and quickly converges to the performance of the centralized approach in a fully-distributed way in terms of job processing latency and network outage probability.

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