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Found 6 papers, 0 papers with code
Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks
Those two methods can effectively control energy consumption and communication cost by controlling the number of local training epochs, local communication, and global communication.
Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction
Although federated learning has achieved many breakthroughs recently, the heterogeneous nature of the learning environment greatly limits its performance and hinders its real-world applications.
Distributed Intelligence in Wireless Networks
In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native-AI wireless networks, with a focus on the basic concepts of native-AI wireless networks, on the AI-enabled edge computing, on the design of distributed learning architectures for heterogeneous networks, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications.
The Role of Machine Learning for Trajectory Prediction in Cooperative Driving
In this paper, we study the role that machine learning can play in cooperative driving.
A Lane Merge Coordination Model for a V2X Scenario
In this paper, we present an application for lane merge coordination based on a centralised system, for connected cars.
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles
Deep Reinforcement Learning and data analysis is used to predict trajectory recommendations for connected vehicles, taking into account unconnected vehicles for those suggestions.
