Training algorithms through Federated Learning has emerged as a promising strategy to safeguard data privacy in distributed environments. this training can be performed on several devices, ranging from high-capacity s...
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ISBN:
(纸本)9783031751097;9783031751103
Training algorithms through Federated Learning has emerged as a promising strategy to safeguard data privacy in distributed environments. this training can be performed on several devices, ranging from high-capacity servers to devices with limited capabilities. However, handling numerous data sources can overload these devices, especially low-power ones, increasing response time. A particular scenario is Virtual Reality, as it requires connection to multiple data sources where latency is critical. Virtual Reality devices have traditionally required a continuous connection to computer equipment, limiting their versatility and the advantages of wireless devices. Recent technological advancements in these devices have increased their computational capabilities, enabling them to perform certain tasks independently. this work addresses the challenge of training a neural network on Virtual Reality devices through a federated system, to develop an enriched collaborative model stored and aggregated in the Cloud. the objective is to evaluate the computational costs and discern the possibilities and limitations of Virtual Reality in Artificial Intelligence.
the proceedings contain 9 papers. the topics discussed include: software troubleshooting using machine learning;the effect of non volatile memory on a distributed storage system;thermal profiling and modeling of Hadoo...
ISBN:
(纸本)9781538614396
the proceedings contain 9 papers. the topics discussed include: software troubleshooting using machine learning;the effect of non volatile memory on a distributed storage system;thermal profiling and modeling of Hadoop clusters using BigData applications;parallel LDA with over-decomposition;characterization of vertex-centric breadth first search for lattice graphs;a perspective on the future of CFD and analysis;high-resolution modeling of multiscale atmospheric convection;numerical simulation of aerospace applications using overset mesh;and performance optimization of OpenFOAM∗ on clusters of Intel® Xeon Phi™ processors.
Edge computing is a rapidly developing research area known for its ability to reduce latency and improve energy efficiency, and it also has a potential for green computing. Many geographically distributed edge servers...
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Federated Learning (FL) has progressed, providing a distributed mechanism where data need not be consolidated, thereby enhancing the privacy and security of sensitive healthcare data. Recent advancements in multimodal...
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Artificial Intelligence of things (AIoT) systems are widely utilized in various domains such as smart logistics and smart health. In AIoT systems, computational tasks earmarked for offloading by IoT devices often have...
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Natural Language Processing (NLP) research has predominantly focused on the English language, leading to a wealth of resources and advancements tailored to English. However, there is a growing need to extend these cap...
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the proceedings contain 35 papers. the topics discussed include: DeepPicar: a low-cost deep neural network-based autonomous car;a case study of cyber-physical system design: autonomous pick-and-place robot;write-aware...
ISBN:
(纸本)9781538677599
the proceedings contain 35 papers. the topics discussed include: DeepPicar: a low-cost deep neural network-based autonomous car;a case study of cyber-physical system design: autonomous pick-and-place robot;write-aware data allocation on heterogeneous memory architecture with minimum cost;AirTight: a resilient wireless communication protocol for mixed-criticality systems;parameterized data reduction framework of thermal sensing for gait velocity measurements;an adaptive computation framework of distributed deep learning models for Internet-of-things applications;and phase-based profiling and performance prediction with timing approximate simulators.
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