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pro vyhledávání: '"learning on edge"'
Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated continual learning (FCL) has eme
Externí odkaz:
http://arxiv.org/abs/2411.13740
Autor:
Pahng, Seong Ho, Hormoz, Sahand
Graph Neural Networks (GNNs) traditionally employ a message-passing mechanism that resembles diffusion over undirected graphs, which often leads to homogenization of node features and reduced discriminative power in tasks such as node classification.
Externí odkaz:
http://arxiv.org/abs/2410.14109
Collaborative perception systems leverage multiple edge devices, such surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their advantages, challenges such as limited channel capacity and data redund
Externí odkaz:
http://arxiv.org/abs/2409.00146
Autor:
Khaliq, Salman Abdul, Hafiz, Rehan
Deep Learning Architectures employ heavy computations and bulk of the computational energy is taken up by the convolution operations in the Convolutional Neural Networks. The objective of our proposed work is to reduce the energy consumption and size
Externí odkaz:
http://arxiv.org/abs/2407.11260
Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of it deployed on an edge device and the rest on a remote server is emerging as a promising approach. It allows the power of DNNs to be leveraged for latency-s
Externí odkaz:
http://arxiv.org/abs/2407.05982
Graph representation learning (GRL) is to encode graph elements into informative vector representations, which can be used in downstream tasks for analyzing graph-structured data and has seen extensive applications in various domains. However, the ma
Externí odkaz:
http://arxiv.org/abs/2406.13369
Edge computing has recently emerged as a promising paradigm to boost the performance of distributed learning by leveraging the distributed resources at edge nodes. Architecturally, the introduction of edge nodes adds an additional intermediate layer
Externí odkaz:
http://arxiv.org/abs/2406.10831
Modern Internet of Things (IoT) applications generate enormous amounts of data, making data-driven machine learning essential for developing precise and reliable statistical models. However, data is often stored in silos, and strict user-privacy legi
Externí odkaz:
http://arxiv.org/abs/2406.05517
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