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IEEE Southeastcon

作者： Ali Abdelmoniem Frank Feng Eric Huang P. L. Carro M. Angeles Losada Alicia Lopez J. Mateo A. Ortega D. Ribas E. Lleida N. Antoniades D. Richards Xin J. Jiang Yi Wang Department of Engineering and Environmental Science College of Staten Island CUNY Staten Island NY Communications Networks and Information Technologies (CeNIT) Aragón Institute of Engineering Research (i3A) Universidad de Zaragoza Zaragoza Spain Photonic Technologies Group (GTF) Aragón Institute of Engineering Research (i3A) Universidad de Zaragoza Zaragoza Spain Voice Input Voice Output Laboratory (ViVoLab) Aragón Institute of Engineering Research (i3A) Universidad de Zaragoza Zaragoza Spain Department of Electrical and Computer Engineering Manhattan College Bronx NY

This study investigates the impact of under-filled and over-filled launching configurations on the transmission properties of Plastic Optical Fiber (POF). Experimental analyses on step and graded index fibers reveal differences in spatial and frequency properties under these launching conditions. Results show that the under-filled configuration leads to narrower radial profiles and Encircled Angular Flux (EAF) compared to the over-filled configuration. Additionally, under-filled launching produces better frequency response and larger bandwidth, particularly notable in shorter fibers. Results show that the under-filled launching significantly improves transmission properties, offering potential improvements in POF applications.

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Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches

arXiv

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arXiv 2024年

作者： Dong, Yanjie Zhang, Haijun Li, Chengming Guo, Song Leung, Victor C.M. Hu, Xiping Artificial Intelligence Research Institute Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen518172 China The Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The School of Medical Technology Beijing Institute of Technology Beijing100811 China

Since the invention of GPT2-1.5B in 2019, large language models (LLMs) have transitioned from specialized models to versatile foundation models. The LLMs exhibit impressive zero-shot ability, however, require fine-tuning on local datasets and significant resources for deployment. Traditional fine-tuning techniques with the first-order optimizers require substantial GPU memory that exceeds mainstream hardware capability. Therefore, memory-efficient methods are motivated to be investigated. Model compression techniques can reduce energy consumption, operational costs, and environmental impact so that to support sustainable artificial intelligence advancements. Additionally, large-scale foundation models have expanded to create images, audio, videos, and multi-modal contents, further emphasizing the need for efficient deployment. Therefore, we are motivated to present a comprehensive overview of the prevalent memory-efficient fine-tuning methods over the network edge. We also review the state-of-the-art literatures on model compression to provide a vision on deploying LLMs over the network edge. © 2024, CC BY.

关键词： Edge computing

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P-FedAvg: Parallelizing federated learning with theoretical guarantees 40

P-FedAvg: Parallelizing federated learning with theoretical ...

引用

40th IEEE Conference on computer Communications, INFOCOM 2021

作者： Zhong, Zhicong Zhou, Yipeng Wu, Di Chen, Xu Chen, Min Li, Chao Sheng, Quan Z. Sun Yat-Sen University School of Computer Science and Engineering Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China Department of Computing Macquarie University SydneyNSW2109 Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Tencent Technology Co. Ltd Shenzhen China

ISBN: (纸本)9780738112817

With the growth of participating clients, the centralized parameter server (PS) will seriously limit the scale and efficiency of Federated Learning (FL). A straightforward approach to scale up the FL system is to construct a Parallel FL (PFL) system with multiple PSes. However, it is unclear whether PFL can really achieve a faster convergence rate or not. Even if the answer is yes, it is non-trivial to design a highly efficient parameter average algorithm for a PFL system. In this paper, we propose a completely parallelizable FL algorithm called P-FedAvg under the PFL architecture. P-FedAvg extends the well-known FedAvg algorithm by allowing multiple PSes to cooperate and train a learning model together. In P-FedAvg, each PS is only responsible for a fraction of total clients, but PSes can mix model parameters in a dedicatedly designed way so that the FL model can well converge. Different from heuristic-based algorithms, P-FedAvg is with theoretical guarantees. To be rigorous, we conduct theoretical analysis on the convergence rate of P-FedAvg, and derive the optimal weights for each PS to mix parameters with its neighbors. We also examine how the overlay topology formed by PSes affects the convergence rate and robustness of a PFL system. Lastly, we perform extensive experiments with real datasets to verify our analysis and demonstrate that P-FedAvg can significantly improve convergence rates than traditional FedAvg and other competitive baselines. We believe that our work can help to lay a theoretical foundation for building more efficient PFL systems. © 2021 IEEE.

关键词： Network topology

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SVFL: Efficient Secure Aggregation and Verification for Cross-Silo Federated Learning

TechRxiv

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TechRxiv 2022年

作者： Luo, Fucai Al-Kuwari, Saif Ding, Yong Department of New Networks Peng Cheng Laboratory Shenzhen518055 China College of Science and Engineering Hamad Bin Khalifa University Doha999043 Qatar Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Cross-silo federated learning (FL) allows organizations to collaboratively train machine learning (ML) models by sending their local gradients to a server for aggregation, without having to disclose their data. The main security issues in FL, that is, the privacy of the gradient and the trained model, and the correctness verification of the aggregated gradient, are gaining increasing attention from industry and academia. A popular approach to protect the privacy of the gradient and the trained model is for each client to mask their own gradients using additively homomorphic encryption (HE). However, this leads to significant computation and communication overhead. On the other hand, to verify the aggregated gradient, verifiable FL that requires the server to provide a verifiable aggregated gradient was proposed. However, existing verifiable FL protocols perform poorly in computation and communication. In this paper, we propose SVFL, an efficient protocol for cross-silo FL, that supports both secure gradient aggregation and verification. We first replace the heavy HE operations with a simple masking technique. Then, we design an efficient verification mechanism that achieves the correctness verification of the aggregated gradient. We evaluate the performance of SVFL and show, by complexity analysis and experimental evaluations, that its computation and communication overhead remain low even on large datasets, with a negligible accuracy loss (less than 1%). Furthermore, we conduct an experimental comparison between SVFL and other existing FL protocols, and show that SVFL achieves significant efficiency improvements in both computation and communication. © 2022, CC BY.

关键词： Machine learning

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Hybrid-integrated high-performance microwave photonic filter with switchable response

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Photonics Research 2021年第8期9卷 1569-1580页

作者： YUANSHENG TAO HAOWEN SHU XINGJUN WANG MING JIN ZIHAN TAO FENGHE YANG JINGBO SHI JUN QIN State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Frontier Science Center for Nano-optoelectronics Peking UniversityBeijing 100871China Peking University Yangtze Delta Institute of Optoelectronics Nantong 226010China Peng Cheng Laboratory Shenzhen 518055China

The integrated microwave photonic filter(MPF),as a compelling candidate for next-generation radio-frequency(RF)applications,has been widely investigated for ***,most integrated MPFs reported thus far have merely incorporated passive photonic components onto a chip-scale platform,while all necessary active devices are still bulk and *** few attempts to higher photonic integration of MPFs have been executed,the achieved filtering performances are fairly limited,which impedes the pathway to practical ***,we demonstrate,for the first time to our knowledge,an all-integrated MPF combined with high filtering performances,through hybrid integration of an In P chip-based laser and a monolithic silicon photonic circuit consisting of a dual-drive Mach–Zehnder modulator,a high-Q ring resonator,and a *** integrated MPF exhibits a high spectral resolution as narrow as 360 MHz,a wide-frequency tunable range covering the S-band to K-band(3 to 25 GHz),and a large rejection ratio of>40 d ***,the filtering response can be agilely switched between the bandpass and band-stop function with a transient respond time(48μs).Compared with previous MPFs in a similar integration level,the obtained spectral resolution in this work is dramatically improved by nearly one order of magnitude,while the valid frequency tunable range is broadened more than twice,which can satisfy the essential filtering requirements in actual RF *** a paradigm demonstration oriented to real-world scenarios,high-resolution RF filtering of realistic microwave signals aiming for interference rejection and channel selection is *** work points out a feasible route to a miniaturized,high-performance,and cost-effective MPF leveraging hybrid integration approach,thus enabling a range of RF applications from wireless communication to radar toward the higher-frequency region,more compact size,and lower power consumption.

关键词： filter modulator switched

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Design and Implementation of Real-Time Localization System (Rtls) Based on Uwb and Tdoa Algorithm

SSRN

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SSRN 2022年

作者： Zhang, Fengyun Yang, Li Liu, Yuhuan Ding, Yulong Yang, Shuang-Hua Li, Hao Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Army Engineering University PLA Nanjing China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Science and Technology on Near-surface Detection Laboratory Wuxi China

Nowadays, accurate localization plays an essential role in many fields, like target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and the high cost. So the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive Ultra Wide-Band (UWB) based RTLS is presented in this paper. We introduce the architecture of real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and analyze the relationship between anchor deployment and positioning accuracy. The Extended Kalman Filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy in moving target. © 2022, The Authors. All rights reserved.

关键词： Extended Kalman filters

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NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery

arXiv

引用

arXiv 2022年

作者： Lu, Ming Fang, Leyuan Li, Muxing Zhang, Bob Zhang, Yi Ghamisi, Pedram College of Electrical and Information Engineering Hunan University Changsha410082 China State Key Laboratory of Integrated Services Networks Xidian University Xian710126 China Peng Cheng Laboratory Shenzhen518000 China College of Engineering & Computer Science Australian National University ACT2601 Australia Department of Computer and Information Science University of Macau 999078 China College of Computer Science Sichuan University Chengdu610065 China Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Vienna1030 Austria

The use of deep learning for water extraction requires precise pixel-level labels. However, it is very difficult to label high-resolution remote sensing images at the pixel level. Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet). Compared with pixel-level labels, point labels are much easier to obtain, but they will lose much information. In this paper, we take advantage of the similarity between the adjacent pixels of a local water-body, and propose a neighbor sampler to resample remote sensing images. Then, the sampled images are sent to the network for feature aggregation. In addition, we use an improved recursive training algorithm to further improve the extraction accuracy, making the water boundary more natural. Furthermore, our method utilizes neighboring features instead of global or local features to learn more representative features. The experimental results show that the proposed NFANet method not only outperforms other studied weakly supervised approaches, but also obtains similar results as the state-of-the-art ones. © 2022, CC BY.

关键词： Semantic Segmentation

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A lightweight identity authentication scheme for UAV and road base stations

A lightweight identity authentication scheme for UAV and roa...

引用

2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Li, Yinyin Du, Xiaoyu Zhou, Sufang International Joint Research Laboratory for Cooperative Vehicular Networks of Henan Kaifeng Henan University Kaifeng Henan China School of Computer and Information Engineering Henan University Kaifeng Henan China

ISBN: (纸本)9781450387828

At present, unmanned aerial vehicles (UAVs) have been widely used in civilian fields (such as smart cities). However, the external environment of the UAV network is complex and computing resources are limited, so it is vulnerable to serious security threats, such as replay attacks, forgery attacks, man-in-the-middle attacks. Seriously, it will cause great damage to the work of the UAV in smart cities. Aiming at the problem of transmission instruction data leakage caused by malicious UAV in communication between road base stations and UAVs, this paper proposes a lightweight identity authentication scheme based on elliptic curve cryptography (ECC). The purpose is to ensure the identity authentication of the UAV and the road base station, to ensure that the mission instructions received by the UAV are authentic and reliable, and to ensure the privacy of the UAV's identity information. The algorithm mainly includes the system initialization phase, initializing the UAV and road base station, and the identity authentication phase. Compared with the traditional identity authentication method, this method has the characteristics of low computational cost, short key and high security, and is more suitable for UAV communication. © 2020 ACM.

关键词： Base stations

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Real-Time and IoT – Why and How?

Real-Time and IoT – Why and How?

引用

CSI Symposium on Real-Time and Embedded Systems and Technologies (RTEST)

作者： Lothar Thiele Swiss Federal Institute of Technology Zurich Computer Engineering and Networks Laboratory

ISBN: (数字)9781728175515

ISBN: (纸本)9781728175522

If visions and forecasts of industry come true then billions of interconnected embedded devices will soon surround us. We have the legitimate expectation that the individual devices as well as the overall system behaves in a reliable and predictable manner. We expect correct and timely results from sensing, computation, communication and actuation due to economic importance or even catastrophic consequences if the overall system is not working correctly. It will be argued that one of the major reasons for unpredictable and unwanted behavior of IoT systems is due to interference from external or internal processes. We need novel architectural concepts, an associated design process and validation strategies to satisfy the strongly conflicting requirements and associated design challenges of platforms for IoT: handle at the same time limited available resources, adaptive run-time behavior, and predictability. The presentation will introduce recent advances in this direction.

关键词： Real-time systems Industries computer network reliability Reliability Sensors Economics Interference

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PPT: A privacy-preserving global model training protocol for federated learning in P2P networks

arXiv

引用

arXiv 2021年

作者： Chen, Qian Wang, Zilong Zhang, Wenjing Lin, Xiaodong The State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xidian University Xi'an China The School of Computer Science University of Guelph Guelph Canada

The concept of Federated Learning (FL) has emerged as a convergence of machine learning, information, and communication technology. It is vital to the development of machine learning, which is expected to be fully decentralized, privacy-preserving, secure, and robust. However, general federated learning settings with a central server can't meet requirements in decentralized environment. In this paper, we propose a decentralized, secure and privacy-preserving global model training protocol, named PPT, for federated learning in Peer-to-peer (P2P) networks. PPT uses a one-hop communication form to aggregate local model update parameters and adopts the symmetric cryptosystem to ensure security. It is worth mentioning that PPT modifies the Eschenauer-Gligor (E-G) scheme to distribute keys for encryption. In terms of privacy preservation, PPT generates random noise to disturb local model update parameters. The noise is eliminated ultimately, which ensures the global model performance compared with other noise-based privacy-preserving methods in FL, e.g., differential privacy. PPT also adopts Game Theory to resist collusion attacks. Through extensive analysis, we demonstrate that PPT various security threats and preserve user privacy. Ingenious experiments demonstrate the utility and efficiency as well. Copyright © 2021, The Authors. All rights reserved.

关键词： Peer to peer networks

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