TDMA-NOMA Based Computation Offloading for Cognitive Capacity Harvesting Networks With Transmission Order Optimization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Communications"
DOI: 10.1109/tcomm.2022.3188841
Abstract: In this paper, we investigate the resource allocation of mobile edge computing (MEC) in cognitive capacity harvesting networks (CCHNs) when non-orthogonal multiple-access (NOMA) technique is adopted. Different from traditional studies for NOMA-MEC networks, we aim… read more here.
Keywords: noma; transmission order; transmission; order optimization ... See more keywords
Designing Universally-Approximating Deep Neural Networks: A First-Order Optimization Approach
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2024.3380007
Abstract: Universal approximation capability, also referred to as universality, is an important property of deep neural networks, endowing them with the potency to accurately represent the underlying target function in learning tasks. In practice, the architecture… read more here.
Keywords: deep neural; order optimization; first order; neural networks ... See more keywords
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2022.3214122
Abstract: Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data. To enhance the training efficiency of FL, various algorithms have… read more here.
Keywords: federated learning; fedzo; order optimization; order ... See more keywords