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Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization
Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications.
Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles
Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles.
Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment
Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks?
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