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Found 5 papers, 0 papers with code
FedAgg: Adaptive Federated Learning with Aggregated Gradients
To surmount the obstacle that acquiring other clients' local information, we introduce the mean-field approach by leveraging two mean-field terms to approximately estimate the average local parameters and gradients over time in a manner that precludes the need for local information exchange among clients and design the decentralized adaptive learning rate for each client.
Adaptive Federated Learning via New Entropy Approach
In this paper, by leveraging entropy as a new metric for assessing the degree of system disorder, we propose an adaptive FEDerated learning algorithm based on ENTropy theory (FedEnt) to alleviate the parameter deviation among heterogeneous clients and achieve fast convergence.
Incentive Mechanism and Path Planning for UAV Hitching over Traffic Networks
In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles.
Dynamic Pricing and Mean Field Analysis for Controlling Age of Information
This dynamic pricing design problem needs to well balance the monetary payments as rewards to users and the AoI evolution over time, and is challenging to solve especially under the incomplete information about users' arrivals and their private sampling costs.
Multi-UAV Cooperative Trajectory for Servicing Dynamic Demands and Charging Battery
To our best knowledge, this paper is the first to design and analyze cooperative path planning algorithms of a large UAV swarm for optimally servicing many spatial locations, where ground users' demands are released dynamically in the long time horizon.
Networking and Internet Architecture
