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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zaid J. Towfic Jianshu Chen Ali H. Sayed Electrical Engineering Department University of California Los Angeles

ISBN: (纸本)9781479903573

We study the distributed inference task over regression and classification models where the likelihood function is strongly log-concave. We show that diffusion strategies allow the KL divergence between two likelihood functions to converge to zero at the rate 1/Ni on average and with high probability, where N is the number of nodes in the network and i is the number of iterations. We derive asymptotic expressions for the expected regularized KL divergence and show that the diffusion strategy can outperform both non-cooperative and conventional centralized strategies, since diffusion implementations can weigh a node's contribution in proportion to its noise level.

关键词： distributed regression distributed classification diffusion adaptation Kullback-Leibler divergence relative entropy relative entropy Likelihood Functions Ganglia Noise levels diffusion iterative methods Divergence divergence Divergence divergence Taxonomy

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Power-Aware Activity Monitoring Using distributed Wearable Sensors

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IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2014年第4期44卷 537-544页

作者： Ghasemzadeh, Hassan Panuccio, Pasquale Trovato, Simone Fortino, Giancarlo Jafari, Roozbeh Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Univ Calabria Dipartimento Informat Elettron & Sistemist I-87036 Arcavacata Di Rende Italy Univ Texas Dallas Dept Elect Engn Richardson TX 75080 USA

Monitoring human movements using wireless wearable sensors finds applications in a variety of domains including healthcare and wellness. In these systems, sensory devices are tightly integrated with the human body and infer status of the user through signal and information processing. Typically, highly accurate observations can be made at the cost of deploying a sufficiently large number of sensors, which in turn results in increased energy consumption of the system and reduced adherence to using the system. Therefore, optimizing power consumption of the system while maintaining acceptable accuracy plays a crucial role in realizing these stringent resource constraint systems. In this paper, we present an activity monitoring approach that minimizes power consumption of the system subject to a lower bound on the classification accuracy. The system utilizes computationally simple template-matching blocks that perform classifications on individual sensor nodes. The system further employs a boosting approach to enhance accuracy of the distributed classifier by selecting a subset of sensors optimized in terms of power consumption and capable of achieving a given lower bound accuracy criterion. A proof-of-concept evaluation with three participants performing 14 transitional actions was conducted, where collected signals were segmented and labeled manually for each action. The results indicated that the proposed approach provides more than a 65% reduction in the power consumption of the signal processing, while maintaining 80% sensitivity in classifying human movements.

关键词： Action recognition AdaBoost distributed classification low-power design real-time embedded systems signal processing wearable computing

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distributed Inference in Tree Networks Using Coding Theory

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第14期63卷 3715-3726页

作者： Kailkhura, Bhavya Vempaty, Aditya Varshney, Pramod K. Syracuse Univ Dept Elect & Comp Engn Syracuse NY 13244 USA

In this paper, we consider the problem of distributed inference in tree based networks. In the framework considered in this paper, distributed nodes make a 1-bit local decision regarding a phenomenon before sending it to the fusion center (FC) via intermediate nodes. We propose the use of coding theory based techniques to solve this distributed inference problem in such structures. Data is progressively compressed as it moves towards the FC. The FC makes the global inference after receiving data from intermediate nodes. Data fusion at nodes as well as at the FC is implemented via error correcting codes. In this context, we analyze the performance for a given code matrix and also design the optimal code matrices at every level of the tree. We address the problems of distributed classification and distributed estimation separately and develop schemes to perform these tasks in tree networks. The proposed schemes are of practical significance due to their simple structure. We study the asymptotic inference performance of our schemes for two different classes of tree networks: fixed height tree networks, and fixed degree tree networks. We derive the sufficient conditions under which the proposed schemes are asymptotically optimal.

关键词： distributed classification distributed estimation error correcting codes information fusion tree networks wireless sensor networks

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Learning From Heterogeneous Data Based on Social Interactions Over Graphs

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IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第5期69卷 3347-3371页

作者： Bordignon, Virginia Vlaski, Stefan Matta, Vincenzo Sayed, Ali H. Ecole Polytech Fed Lausanne EPFL Sch Engn CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne EPFL Adapt Syst Lab CH-1015 Lausanne Switzerland Imperial Coll London Dept Elect & Elect Engn London SW7 2BT England Univ Salerno Dept Informat & Elect Engn & Appl Math DIEM I-84084 Fisciano Italy Natl Interuniv Consortium Telecommun CNIT Rome Italy

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning, several useful strategies have been developed, which solve decision making problems through local cooperation across distributed agents and allow them to learn from streaming data. However, traditional social learning strategies rely on the fundamental assumption that each agent has significant prior knowledge of the underlying distribution of the observations. In this work we overcome this issue by introducing a machine learning framework that exploits social interactions over a graph, leading to a fully data-driven solution to the distributed classification problem. In the proposed social machine learning (SML) strategy, two phases are present: in the training phase, classifiers are independently trained to generate a belief over a set of hypotheses using a finite number of training samples;in the prediction phase, classifiers evaluate streaming unlabeled observations and share their instantaneous beliefs with neighboring classifiers. We show that the SML strategy enables the agents to learn consistently under this highly-heterogeneous setting and allows the network to continue learning even during the prediction phase when it is deciding on unlabeled samples. The prediction decisions are used to continually improve performance thereafter in a manner that is markedly different from most existing static classification schemes where, following training, the decisions on unlabeled data are not re-used to improve future performance.

关键词： Training Data models Random variables Videos Predictive models Machine learning algorithms distributed databases distributed classification social learning neural networks diffusion strategies

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A Computational model based on Cooperative Mobile Agents for Big Data image Segmentation 2

A Computational model based on Cooperative Mobile Agents for...

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2014 Second World Conference on Complex Systems (WCCS)

作者： Benchara, Fatema Zahra Youssfi, Mohamed Bouattane, Omar Ouajji, Hassan Bensalah, Mohammed Ouadi Univ Hassan II Casablanca ENSET Mohammedia Lab SSDIA Casablanca Morocco Univ Mohammed V AGDAL FSR Rabat Morocco

ISBN: (纸本)9781479946471

The aim of this paper is to present interesting results obtained by implementing big data classification algorithm using a cooperative mobile agents model. In this work we focused on the application of this classification for Magnetic Resonance Images (MRI) segmentation which is presented based on parallel fine grained c-means algorithm. This algorithm is converged to distributed classification c-means algorithm in order to be implemented on a parallel and distributed virtual machine based on mobile agents. The big data MRI image to be processed is split into small elementary images by the host agent. The obtained list of item images is distributed to mobile agents deployed on each node of the distributed system. Each agent is asked to perform the execution of the c-means algorithm using its assigned image item and return its elementary results to the host agent which computes the current global class centers and newly distributes them. This process is repeated until the convergence of the distributed algorithm. The experimental results show that the distributed classification method can improve the big data segmentation.

关键词： Image Segmentation c-means algorithm mobile agent distributed classification multi agent system Cardiac MRI image

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Privacy preserving naive Bayes classifier for vertically partitioned data

Privacy preserving naive Bayes classifier for vertically par...

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4th SIAM International Conference on Data Mining

作者： Vaidya, J Clifton, C Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)0898715687

Privacy-Preserving Data Mining - developing models without seeing the data - is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. Naive Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This paper brings privacy-preservation to Naive Bayes classification on vertically partitioned data.

关键词： privacy security distributed classification

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On Classifying Drifting Concepts in P2P Networks

On Classifying Drifting Concepts in P2P Networks

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Ang, Hock Hee Gopalkrishnan, Vivekanand Ng, Wee Keong Hoi, Steven Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9783642158797

Concept drift is a common challenge for many real-world data mining and knowledge discovery applications. Most of the existing studies for concept drift are based on centralized settings, and are often hard to adapt in a distributed computing environment. In this paper, we investigate a new research problem, P2P concept drift detection, which aims to effectively classify drifting concepts in P2P networks. We propose a novel P2P learning framework for concept drift classification, which includes both reactive and proactive approaches to classify the drifting concepts in a distributed manner. Our empirical study shows that the proposed technique is able to effectively detect the drifting concepts and improve the classification performance.

关键词： Concept drift classification peer-to-peer (P2P) networks distributed classification

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Vehicle acoustic classification in netted sensor systems using Gaussian mixture models

Vehicle acoustic classification in netted sensor systems usi...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XIV

作者： Necioglu, BF Christou, CT George, EB Jacyna, CM Mitre Corp Mclean VA 22102 USA

ISBN: (纸本)0819457949

Acoustic vehicle classification is a difficult problem due to the non-stationary nature of the signals, and especially the lack of strong harmonic structure for most civilian vehicles with highly muffled exhausts. Acoustic signatures will also vary largely depending on speed, acceleration, gear position, and even the aspect angle of the sensor. The problem becomes more complicated when the deployed acoustic sensors have less than ideal characteristics, in terms of both the frequency response of the transducers, and hardware capabilities which determine the resolution and dynamic range. In a hierarchical network topology, less capable Tier 1 sensors can be tasked with reasonably sophisticated signal processing and classification algorithms, reducing energy-expensive communications with the upper layers. However, at Tier 2, more sophisticated classification algorithms exceeding the Tier 1 sensor/processor capabilities can be deployed. The focus of this paper is the investigation of a Gaussian mixture model (GMM) based classification approach for these upper nodes. The use of GMMs is motivated by their ability to model arbitrary distributions, which is very relevant in the case of motor vehicles with varying operation modes and engines. Tier 1 sensors acquire the acoustic signal and transmit computed feature vectors up to Tier 2 processors for maximum-likelihood classification using GMMs. In a binary classification task of light-vs-heavy vehicles, the GMM based approach achieves 7% equal error rate, providing an approximate error reduction of 49% over Tier 1 only approaches.

关键词： acoustics feature extraction Gaussian mixture models distributed classification

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NETWORK CLASSIFIERS BASED ON SOCIAL LEARNING

NETWORK CLASSIFIERS BASED ON SOCIAL LEARNING

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Bordignon, Virginia Vlaski, Stefan Matta, Vincenzo Sayed, Ali H. Ecole Polytech Fed Lausanne EPFL Sch Engn Lausanne Switzerland Univ Salerno DIEM Fisciano Italy

ISBN: (纸本)9781728176055

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to streaming data during testing and continue to improve their performance even during this phase. By doing so, the proposed architecture is able to improve prediction performance over time with unlabeled data. Inspired by social learning algorithms, which require prior knowledge of the observations distribution, we propose a Social Machine Learning (SML) paradigm that is able to exploit the imperfect models generated during the learning phase. We show that this strategy results in consistent learning with high probability, and it yields a robust structure against poorly trained classifiers. Simulations with an ensemble of feedforward neural networks are provided to illustrate the theoretical results.

关键词： distributed classification social learning combination of classifiers neural networks

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ACCELERATED UNSUPERVISED CLUSTERING IN ACOUSTIC SENSOR NETWORKS USING FEDERATED LEARNING AND A VARIATIONAL AUTOENCODER 17

ACCELERATED UNSUPERVISED CLUSTERING IN ACOUSTIC SENSOR NETWO...

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17th International Workshop on Acoustic Signal Enhancement (IWAENC)

作者： Becker, Luca Nelus, Alexandra Glitza, Rene Martin, Rainer Ruhr Univ Bochum Inst Commun Acoust Bochum Germany

ISBN: (数字)9781665468671

ISBN: (纸本)9781665468671

In this paper we present an accelerated algorithm for clustering source-dominated microphones in acoustic sensor networks. Predicated on privacy-preserving unsupervised clustered federated learning that groups microphones by evaluating the similarity of model weight updates, we introduce a light-weight variational autoencoder and equip the algorithm with supplementary control criteria for faster convergence. We validate the quality, degree of acceleration and utility of our method using clustering-based and classification-based tasks. Compared to the previously employed deterministic autoencoder, we observe a significantly lower number of client-server communication rounds at the price of a minor reduction in clustering performance.

关键词： clustered federated learning DNN autoencoder acoustic sensor networks distributed classification

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