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Found 7 papers, 1 papers with code
MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates
This work proposes a Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 updates, called MKOR, that improves the training time and convergence properties of deep neural networks (DNNs).
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design
In this paper, a green-quantized FL framework, which represents data with a finite precision level in both local training and uplink transmission, is proposed.
On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks
In this paper, a quantized FL framework, that represents data with a finite level of precision in both local training and uplink transmission, is proposed.
A Deep Reinforcement Learning Approach to Efficient Drone Mobility Support
In this paper, we propose a novel handover framework for providing efficient mobility support and reliable wireless connectivity to drones served by a terrestrial cellular network.
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition.
Efficient Drone Mobility Support Using Reinforcement Learning
Flying drones can be used in a wide range of applications and services from surveillance to package delivery.
Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication
Formally, the URLLC resource allocation problem is posed as a power minimization problem under reliability, latency, and rate constraints.
