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FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation
This diversity can critically affect the training pace and the performance of models.
Application of End-to-End Deep Learning in Wireless Communications Systems
In this article, we present a basic concept of the deep learning and its application to WCS by investigating the resource allocation (RA) scheme based on a deep neural network (DNN) where multiple goals with various constraints can be satisfied through the end-to-end deep learning.
Building Encoder and Decoder with Deep Neural Networks: On the Way to Reality
Deep learning has been a groundbreaking technology in various fields as well as in communications systems.
Information Theory Signal Processing Information Theory
