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IEEE Conference on Global Communications (GLOBECOM)

作者： Omar Alhussein Moshi Wei Arashmid Akhavain Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Advanced Networking Team Huawei Ottawa Research & Development Centre Ottawa Canada Department of Computer Science York University Toronto Canada

Split learning is a privacy-preserving distributed learning paradigm in which an ML model (e.g., a neural network) is split into two parts (i.e., an encoder and a decoder). The encoder shares so-called latent representation, rather than raw data, for model training. In mobile-edge computing, network functions (such as traffic forecasting) can be trained via split learning where an encoder resides in a user equipment (UE) and a decoder resides in the edge network. Based on the data processing inequality and the information bottleneck (IB) theory, we present a new framework and training mechanism to enable a dynamic balancing of the transmission resource consumption with the informativeness of the shared latent representations, which directly impacts the predictive performance. The proposed training mechanism offers an encoder-decoder neural network architecture featuring multiple modes of complexity-relevance tradeoffs, enabling tunable performance. The adaptability can accommodate varying real-time network conditions and application requirements, potentially reducing operational expenditure and enhancing network agility. As a proof of concept, we apply the training mechanism to a millimeter-wave (mmWave)-enabled throughput prediction problem. We also offer new insights and highlight some challenges related to recurrent neural networks from the perspective of the IB theory. Interestingly, we find a compression phenomenon across the temporal domain of the sequential model, in addition to the compression phase that occurs with the number of training epochs.

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FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection

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IEEE Transactions on Mobile Computing 2025年

作者： Geng, Jiaxiang Li, Boyu Qin, Xiaoqi Li, Yixuan Li, Liang Hou, Yanzhao Pan, Miao Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Taiyuan University of Technology College of Electronic Information Engineering Taiyuan030024 China Peng Cheng Laboratory Department of Advanced Interdisciplinary Research 518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States

Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping local gradient transmission with continuous local computing, FL can remarkably reduce its training latency over homogeneous clients, yet encounter severe model staleness, model drifts, memory cost and straggler issues in heterogeneous environments. To unleash the full potential of overlapping, we propose, FedEx, a novel federated learning approach to expedite FL training over mobile devices under data, computing and wireless heterogeneity. FedEx redefines the overlapping procedure with staleness ceilings to constrain memory consumption and make overlapping compatible with participation selection (PS) designs. Then, FedEx characterizes the PS utility function by considering the latency reduced by overlapping, and provides a holistic PS solution to address the straggler issue. FedEx also introduces a simple but effective metric to trigger overlapping, in order to avoid model drifts. Experimental results show that compared with its peer designs, FedEx demonstrates substantial reductions in FL training latency over heterogeneous mobile devices with limited memory cost. © 2002-2012 IEEE.

关键词： Federated learning Heterogeneous mobile devices Overlapping computing and communications Participant selection

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Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory

arXiv

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arXiv 2023年

作者： Alhussein, Omar Wei, Moshi Akhavain, Arashmid Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Advanced Networking Team Huawei Ottawa Research & Development Centre Ottawa Canada Department of Computer Science York University Toronto Canada

关键词： Recurrent neural networks

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Numerical Analysis of Soliton Propagation in a Tapered Waveguide

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Journal of Optical Communications 2023年第4期44卷 535-538页

作者： Raja, M.A. Ranathive, S. Sivaram, M. Krishna Kumar, L. Vinoth Kumar, K. Amiri, Iraj S Department of Biomedical Engineering Dr. N.G.P. Institute of Technology Coimbatore India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Networking Lebanese French University Kurdistan Region Erbil Iraq Department of CSE Nehru Institute of Engineering & Technology Coimbatore India Department of EEE Karunya Institute of Technology and Sciences Coimbatore India Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam

In this paper, dispersion decreased profiled tapered fiber is designed whose dispersion characteristics and soliton propagation is investigated numerically using Darboux transformation. The result reveals that solitons pulse gets compression as it propagates along the length of the tapered region. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Dispersion (waves)

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Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli Dong Hongjian Liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems

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Enabling Support for TLS and TLS Fingerprinting in Network Stack Tester

Enabling Support for TLS and TLS Fingerprinting in Network S...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Nitin Dabi Mohit P. Tahiliani Vishal J. Rathod Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore Karnataka India Internet of Things - Research and Development Group Centre for Development of Advanced Computing (C-DAC) Bengaluru Karnataka India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Transport Layer Security (TLS) is the standard protocol widely used for securing client-server communications over the Internet. It ensures the confidentiality and integrity of data transmitted between systems. With the rising deployment of TLS 1.3, which is the latest version of the protocol, stronger encryption and improved security features are introduced. The adoption of TLS 1.3 brings about the need for techniques such as TLS fingerprinting. TLS fingerprinting involves analyzing the unique characteristics of TLS handshakes to identify and classify different implementations or configurations of TLS. However, researchers currently lack a dedicated platform to test TLS and TLS fingerprinting techniques in a controlled environment. To address this gap, we aim to enhance NeST (Network Stack Tester) by adding support for TLS and TLS fingerprinting. NeST is a Python tool for network emulation that internally uses Linux network namespaces and Linux utilities to create virtual testbeds, perform experiments using various network protocols and collect statistics. By integrating TLS and TLS fingerprinting into NeST, we provide an easy-to-use tool that enables researchers to experiment with TLS, analyze TLS traffic, and evaluate the effectiveness of TLS fingerprinting techniques.

关键词： Protocols Linux Emulation Fingerprint recognition Communications technology Internet Encryption

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Design and Development of an Emulation Model for VPN and VPN Bonding

Design and Development of an Emulation Model for VPN and VPN...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Kaustubh Sharma Mohit P. Tahiliani Vishal J. Rathod Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore Karnataka India Internet of Things - Research and Development Group Centre for Development of Advanced Computing (C-DAC) Bengaluru Karnataka India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Virtual Private Networks (VPNs) have become indispensable for organizations seeking secure remote network access, with a significant rise in their adoption. While the COVID-19 pandemic initially fueled the surge in remote work and VPN usage, the trend has continued post-pandemic as organizations increasingly opt for hybrid work models. VPNs allow users to establish secure connections to their organization’s network from any location, ensuring the confidentiality of transmitted data. Moreover, VPN bonding, which combines multiple VPN connections into a unified interface, improves performance and reliability, particularly in areas with limited Internet connectivity. As the usage of VPN and VPN bonding technologies continues to expand, a growing demand arises for research in this field, and consequently, the need for robust emulators and simulators. However, existing network emulators or simulators currently lack comprehensive support for VPN-related technologies. To address these limitations, this paper aims to develop intuitive and user-friendly Application Programming Interfaces (APIs) for emulating VPN and VPN bonding in Network Stack Tester (NeST), a powerful Python package designed to facilitate network emulation for experienced researchers and individuals new to the field. This work utilizes OpenVPN, a secure tunneling daemon, to integrate the support of VPN emulation in NeST. By augmenting the capabilities of NeST, this work intends to provide researchers with a valuable toolset for exploring and testing VPN and VPN bonding technologies across diverse network environments.

关键词： Emulation Organizations Tunneling Remote working Virtual private networks Reliability Bonding

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Accelerating Convolutional Neural Networks via Inter-operator Scheduling

Accelerating Convolutional Neural Networks via Inter-operato...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yi You Pangfeng Liu Ding-Yong Hong Jan-Jan Wu Wei-Chung Hsu Department of Computer Science and Information Engineering National Taiwan University Graduate Institute of Networking and Multimedia National Taiwan University Graduate School of Advanced Technology National Taiwan University Institute of Information Science Academia Sinica

Convolution neural networks (CNNs) are essential in many machine learning tasks. Current deep learning frameworks and compilers usually treat the neutral network as a DAG (directed acyclic graph) of tensor operations and execute them one at a time according to a topological order, which respects the dependency in the DAG. There are two issues with this general approach. First, new CNNs have branch structures, and they form complex DAGs. These DAGs make it hard to find a good topology sort order that schedules operators within a GPU. Second, modern hardware has high computational power, which makes running operators sequentially on modern hardware under-utilizes resources. These two issues open the possibility of exploiting inter-operator parallelism, i.e., parallelism among independent operators in the DAG, to utilize the hardware resources more efficiently. In this work, we formally define the DAG scheduling problem that addresses the resource contention and propose an early-start-time-first algorithm with two heuristic rules for exploiting parallelism between independent operators. Experimental results show that our method improves the performance by up to 3.76× on RTX 3090 compared to the sequential execution.

关键词： Schedules Tensors Processor scheduling Heuristic algorithms Graphics processing units Parallel processing Hardware

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FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection

arXiv

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arXiv 2024年

作者： Geng, Jiaxiang Li, Boyu Qin, Xiaoqi Li, Yixuan Li, Liang Hou, Yanzhao Pan, Miao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Electronic Information Engineering Taiyuan University of Technology Taiyuan030024 China Department of Advanced Interdisciplinary Research Pengcheng Laboratory 518055 China Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States

关键词： Copyrights

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A Privacy Preserving and Byzantine Robust Collaborative Federated Learning Method Design

A Privacy Preserving and Byzantine Robust Collaborative Fede...

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IEEE International Conference on Communications (ICC)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Changchuan Yin Christopher G. Brinton Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.

关键词： Training Privacy Federated learning Simulation Collaboration Clustering algorithms Propagation losses

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