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Found 4 papers, 2 papers with code
Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks
Computational offloading has become an enabling component for edge intelligence in mobile and smart devices.
Learning to Transmit with Provable Guarantees in Wireless Federated Learning
We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks.
Federated Learning with Flexible Control
In this paper, we address this problem and propose FlexFL - an FL algorithm with multiple options that can be adjusted flexibly.
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization
Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint.
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