Cloud Service Providers (CSPs) have recently significantly improved, allowing for outsourcing Machine Learning (ML) training and inference. However, due to the data privacy needs in most of the ML applications, severa...
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ISBN:
(纸本)9781728190549
Cloud Service Providers (CSPs) have recently significantly improved, allowing for outsourcing Machine Learning (ML) training and inference. However, due to the data privacy needs in most of the ML applications, several privacy-preserving technologies, such as Multi-party Computation (MPC), have been proposed to protect the data privacy. MPC offers splitting and exchanging of data among multiple parties, typically managed on cloud environments. Although MPC performs better than other alternatives, it still lags behind regular clear-text ML processing in terms of performance. To reduce the execution time of Privacy-Preserving ML (PPML) via MPC, parallelization of computation and communication among the parties (i.e., pipelined MPC), can be employed. However, the complex nature of these systems makes it challenging to select an optimal network and node configuration for executing a pipelined MPC. To address these challenges, in this paper, we propose a Multi-Objective optimization (MOO) model focusing on achieving optimal configuration to minimize the MPC execution time along with its costs. We formulate an optimization model and propose two distinct approaches to solve it. Our evaluation clearly demonstrates a reduction in execution time and cost with respect to regular MPC execution. The evaluation results also provide valuable insights into the impact of latency and bandwidth considerations on our system's performance, contributing to PPML optimization.
With the rapid development of the fifth-generation wireless communication systems, a profound revolution in terms of transmission capacity, energy efficiency, reliability, latency, and connectivity is highly expected ...
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With the rapid development of the fifth-generation wireless communication systems, a profound revolution in terms of transmission capacity, energy efficiency, reliability, latency, and connectivity is highly expected to support a new batch of industries and applications. To achieve this goal, wireless networks are becoming extremely dynamic, heterogeneous, and complex. The modeling and optimization for the performance of realworld wireless networks are extremely challenging due to the difficulty to predict the network performance as a function of networkparameters, and the prohibitively huge number of parameters to optimize. The conventional network modeling and optimization approaches rely on drive test, trial-and-error, and engineering experience,which are labor intensive, error-prone, and far from optimal. On the other hand, while the research community has spent significant efforts in understanding the fundamental limits of radio channels and developing physical layer techniques to operate close to it, very little is known about the performance limits of wireless networks, where millions of radio channels interact with one another in complex manners. This paper reviews the very recent mathematical and learning based techniques for modeling and optimizing the performance of real-world wireless networks in five aspects, including channel modeling, user demand and traffic modeling, throughput modeling and prediction, network parameter optimization, and IRS empowered performance optimization, and also presents the corresponding notable performance gains.
Planning for Multi-Frequency network (MFN) with Digital Video Broadcasting-Second Generation Terrestrial (DVB-T2) services is particularly useful during the transition from analog to digital broadcasting. For efficien...
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Planning for Multi-Frequency network (MFN) with Digital Video Broadcasting-Second Generation Terrestrial (DVB-T2) services is particularly useful during the transition from analog to digital broadcasting. For efficient management of service area as well as resource allocation, it is necessary to administer the use of networkparameters, e.g., transmit power, frequency channels, and antenna parameters. This article, therefore, proposes a coverage maximization scheme for the MFN with the DVB-T2 system by using the Genetic Algorithm. In order to effectively perform the optimization over regions with diverse geographical structures, a propagation prediction method considering the complex orography together with Digital Elevation Model is used to predict signal strength at each receiving location. Based on the received signal strength and standard Quality of Service criteria, the overall radio coverage of the network is maximized using the optimum values of transmit power levels, frequency channels, and antenna parameters. Although our analysis and simulations are based on the MFN network in Nepal, the methodology presented in this work can be applied in any region with some alterations in geographical and meteorological parameters.
Adjusting parameters of a neural network model to reproduce complete sets of biologically plausible behaviors is a complex task, even in a well-described neural system. We show here a method for evolving a model of th...
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ISBN:
(纸本)9783030014216
Adjusting parameters of a neural network model to reproduce complete sets of biologically plausible behaviors is a complex task, even in a well-described neural system. We show here a method for evolving a model of the mormyrid electromotor command chain to reproduce highly realistic temporal firing patterns as described by neuroethological studies in this system. Our method uses genetic algorithms for tuning unknown parameters in the synapses of the network. The developed fitting function simulates each evolved model under different network inputs and compare its output with the target patterns from the living animal. The obtained synaptic configuration can reveal new information about the functioning of electromotor systems.
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