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Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G
The resulting large-scale complicated network optimization problems are beyond the capability of model-based theoretical methods due to the overwhelming computational complexity and the long processing time.
Asynchronous Wireless Federated Learning with Probabilistic Client Selection
Federated learning (FL) is a promising distributed learning framework where distributed clients collaboratively train a machine learning model coordinated by a server.
Noise Is Useful: Exploiting Data Diversity for Edge Intelligence
Edge intelligence requires to fast access distributed data samples generated by edge devices.
Information Theory Information Theory
