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ZeTFRi - A Zero Trust-Based Free Rider Detection Framework for Next Generation Federated Learning Networks

IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1938-1953页

作者： Edirimannage, Shehan Khalil, Ibrahim Elvitigala, Charitha Daluwatta, Wathsara Wijesekera, Primal Zomaya, Albert Y. RMIT University School of Computing Technologies MelbourneVIC3000 Australia International Computer Science Institute University of California BerkeleyCA94704 United States University of Sydney Centre for Distributed and High-Performance Computing SydneyNSW2006 Australia

With the rapid expansion of next-generation networking, Internet of Things (IoT) devices have become central components of federated learning (FL) networks. FL offers a paradigm for distributed training machine learning models while preserving user data privacy. However, existing network security measures often struggle to identify legitimate contributors from opportunistic free riders within these networks. The Free Rider (FR) problem arises when participants seek to benefit from the FL processes without contributing. In particular, free riders are known to exist within or outside of the network, whereas outside free riders can hardly be identified. The Zero Trust model proposes an environment where no entity, including the network itself, is inherently trusted, providing a foundation to counter external threats seeking to exploit the network. This study proposes a novel framework strengthened by the Zero Trust model to identify external free riders in FL networks. Leveraging a Deep Autoencoding Gaussian Mixture Model (DAGMM)-based technique for internal free rider detection, our framework demonstrates superior performance in identifying free riders across various FR scenarios compared to current state-of-the-art solutions. Through our proposed framework and the principles of Zero Trust, we establish a robust security guarantee for FL networks, ensuring the integrity of the learning process. © 1983-2012 IEEE.

关键词： Federated learning

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A rank-based multiple-choice secretary algorithm for minimising microgrid operating cost under uncertainties

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Frontiers in Energy 2023年第2期17卷 198-210页

作者： Chunqiu Xia Wei Li Xiaomin Chang Ting Yang Albert Y.Zomaya Centre for Distributed and High Performance Computing School of Information TechnologiesThe University of SydneyCamperdownNSW 2006Australia School of Electrical Automation and Information Engineering Tianjin UniversityTianjin 300072China

The increasing use of distributed energy resources changes the way to manage the electricity *** the traditional centralized powered utility,many homes and businesses with local electricity generators have established their own microgrids,which increases the use of renewable energy while introducing a new challenge to the management of the microgrid system from the mismatch and unknown of renewable energy generations,load demands,and dynamic electricity *** address this challenge,a rank-based multiple-choice secretary algorithm(RMSA)was proposed for microgrid management,to reduce the microgrid operating *** than relying on the complete information of future dynamic variables or accurate predictive approaches,a lightweight solution was used to make real-time decisions under *** RMSA enables a microgrid to reduce the operating cost by determining the best electricity purchase timing for each task under dynamic *** experiments were conducted on real-world data sets to prove the efficacy of our solution in complex and divergent real-world scenarios.

关键词： energy management systems demand response scheduling under uncertainty renewable energy sources multiple-choice secretary algorithm

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Design of an Optical Wireless Communication Link Using Mid Wave Infrared Laser Source & Comparison with Short Wave Infrared Laser Sources in Different Atmospheric Anomalies: An Analytical Study

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Nonlinear Optics Quantum Optics 2024年第1-2期60卷 85-111页

作者： Mukherjee, Shibabrata Paul, Sujoy Mazumdar, Saswati Electrical Engineering Department Jadavpur University West Bengal Kolkata700032 India Centre for Distributed Computing Computer Science Engineering Department Jadavpur University West Bengal Kolkata700032 India

Optical Wireless Communication (OWC), sometimes referred to as Free Space Optics (FSO), is the backbone of leading-edge communication systems that offer high performance and fast speed. The main pitfall of this system is that the optical signal is hindered by the different atmospheric anomalies. As a result, the system performance has been distorted and degraded. Among the different atmospheric anomalies like fog, rain, snow, etc., fog is the main enemy of this system. Using the properly chosen optics at the transmitter and receiver sides and wavelength, the system performance may be increased in diverse weather conditions. In this article, four wavelengths like two Mid Wave Infrared (MWIR) (3400 nm, 4100 nm) and two Short Wave Infrared (SWIR) (1550 nm & 2000 nm), 27 dBm optical peak power, the OOK modulation technique, 10 MHz modulation bandwidth, 10-7 BER, 2 km optical wireless link distance have been considered. From the theoretical simulation results, it has been initiated that MWIR optical sources (like 4100 nm and 3400 nm) contribute 72.22 dB, and 71.27 dB link margin at adverse atmospheric anomalies respectively, whereas SWIR Optical sources like 1550 nm & 2000 nm furnish 67.70 dB, 68.50 dB link margin respectively for 2 km link distance. The 4100 nm wavelength Laser source performs better in different atmospheric anomalies and provides a better pointing error angle in clear, as well as adverse weather conditions, compared to other wavelengths. © 2024 Old City Publishing, Inc.

关键词： Space optics

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Latent Block Regression Model 1

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17th Conference of the International Federation of Classification Societies, IFCS 2022

作者： Boutalbi, Rafika Labiod, Lazhar Nadif, Mohamed Institute for Parallel and Distributed Systems Analytic Computing University of Stuttgart Stuttgart Germany Centre Borelli UMR 9010 Université Paris Cité Paris France

ISBN: (数字)9783031090349

ISBN: (纸本)9783031090332

When dealing with high dimensional sparse data, such as in recommender systems,co-clusteringturnsouttobemorebeneficialthanone-sidedclustering,even if one is interested in clustering along one dimension only. Thereby, co-clusterwise is a natural extension of clusterwise. Unfortunately, all of the existing approaches do not consider covariates on both dimensions of a data matrix. In this paper, we propose a Latent Block Regression Model (LBRM) overcoming this limit. For inference, we propose an algorithm performing simultaneously co-clustering and regression where a linear regression model characterizes each block. Placing the estimate of the model parameters under the maximum likelihood approach, we derive a Variational Expectation–Maximization (VEM) algorithm for estimating the model’s parameters. The finality of the proposed VEM-LBRM is illustrated through simulated datasets. © 2023, The Author(s).

关键词： Data mining

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Domain Generalization for Time-Series Forecasting via Extended Domain-Invariant Representations

Domain Generalization for Time-Series Forecasting via Extend...

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Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Yunchuan Shi Wei Li Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

Time-series forecasting is crucial for IoT applications, but generalizing across domains is challenging due to distinct data distributions and dynamics. Most domain generalization methods work well for image processing and classification, but they struggle with time-series forecasting. This is because they solely learn domain-invariant representations of input data, ignoring variations in the output space across domains. This oversight can lead to inaccurate forecasts when outputs in new domains exhibit different distributions or temporal patterns. In this work, we present a new approach to improve the time-series forecasting model generalization by extracting domain-invariant representations from both input and output data. Experiments demonstrate the effectiveness of our approach, achieving significant improvements in forecasting accuracy across multiple test domains. Compared to state-of-the-art methods, our approach delivers up to an 8% increase in accuracy.

关键词： Bridges Accuracy Image processing Time series analysis Predictive models Data models Internet of Things

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Quick-MIMIC:A Multimodal Data Extraction Pipeline for MIMIC with Parallelization

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Big Data Mining and Analytics 2024年第4期7卷 1333-1346页

作者： Yutao Dou Wei Li Yangtao Zheng Xiaojun Yao Huanxiang Liu Albert Y.Zomaya Shaoliang Peng College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082Chinaand also with Centre for Distributed and High Performance ComputingSchool of Computer ScienceThe University of SydneyDarlingtonNSW 2008Australia College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Centre for Distributed and High Performance Computing School of Computer ScienceThe University of SydneyDarlingtonNSW 2008Australia Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China

Medical big data with artificial intelligence are vital in advancing digital ***,the opaque and non-standardised nature embedded in most medical data extraction is prone to batch effects and has become a significant obstacle to reproducing previous *** paper aims to develop an easy-to-use time-series multimodal data extraction pipeline,Quick-MIMIC,for standardised data extraction from MIMIC *** method can fully integrate different data structures into a time-series table,including structured,semi-structured,and unstructured *** also introduce two additional modules to Quick-MIMIC,a pipeline parallelization method and data analysis methods,for reducing the data extraction time and presenting the characteristics of the extracted data *** extensive experimental results show that our pipeline can efficiently extract the needed data from the MIMIC dataset and convert it into the correct format for further analytic tasks.

关键词： MIMIC dataset data extraction pipeline data integration

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ES-Mask: Evolutionary Strip Mask for Explaining Time Series Prediction 37

ES-Mask: Evolutionary Strip Mask for Explaining Time Series ...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Sun, Yifei Song, Cheng Lu, Feng Li, Wei Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia

ISBN: (纸本)9781577358800

Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in understanding model decisions. This paper proposes ES-Mask, a post-hoc and model-agnostic evolutionary strip mask-based saliency approach for time series applications. ES-Mask designs the mask consisting of strips with the same salient value in consecutive time steps to produce binary and sustained feature importance scores over time for easy understanding and interpretation of time series. ES-Mask uses an evolutionary algorithm to search for the optimal mask by manipulating strips in rounds, thus is agnostic to models by involving no internal model states in the search. The initial experiments on MIMIC-III data set show that ES-Mask outperforms state-of-the-art methods. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Time series

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Online Demand Peak Shaving with Machine-Learned Advice in Cyber-Physical Energy Systems

Online Demand Peak Shaving with Machine-Learned Advice in Cy...

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2023 IEEE International Conference on Dependable, Autonomic and Secure computing, 2023 International Conference on Pervasive Intelligence and computing, 2023 International Conference on Cloud and Big Data computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Fengl, Minxi Li, Wei Qin, Boyu Zomaya, Albert Y. Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Xi'an Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment Department of Electrical Engineering China

ISBN: (纸本)9798350304602

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD. © 2023 IEEE.

关键词： Cyber Physical System

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Demystifying Ethereum account diversity:observations,models and analysis

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Frontiers of Computer Science 2022年第4期16卷 119-130页

作者： Chaofan Wang Xiaohai Dai Jiang Xiao Chenchen Li Ming Wen Bingbing Zhou Hai Jin Clusters and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Centre for Distributed and High Performance Computing School of Information TechnologiesThe University of SydneyCamperdownNew South Wales2006Australia

Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart *** massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this *** benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal ***,the exploration of blockchain account diversity remains *** the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are *** this end,we present the first attempt to demystify the account diversity on *** key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the *** then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its ***,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage *** for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total *** latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

关键词： blockchain Ethereum classification account diversity network analysis

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Effect of MIMO scheme on MWIR FSO link & comparison with SWIR wavelength in the presence of different visibility and strong turbulence circumstances

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Journal of Optical Communications 2024年第0期45卷 s1977-s1983页

作者： Mukherjee, Shibabrata Paul, Sujoy Mazumdar, Saswati Electrical Engineering Department Jadavpur University Kolkata700032 India Centre for Distributed Computing Computer Science Engineering Department Jadavpur University Kolkata700032 India

In this article, the authors analyse the performance of the "multiple input multiple output (MIMO)"technique on a free space optical (FSO) communication link using the mid wavelength infrared (MWIR) and commonly used short wavelength infrared (SWIR) in the presence of various visibility conditions like clear weather, light haze, dense haze, fog, etc., and strong turbulence regime. A 5km link range has been considered with different diversity MIMO schemes. The performance analysis has been made for both wavelengths in terms of bit error rate (BER) and link outage probability. As the MIMO diversity is increased, the performance has been improved compared to the single input single output (SISO) scheme. In the presence of foggy conditions and higher turbulence strength, the MWIR wavelength works properly, whereas the SWIR wavelength has not worked for the same signal to noise (SNR) ratio and the same link distance. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Visibility

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