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Found 4 papers, 0 papers with code
An Incentive Mechanism for Federated Learning Based on Multiple Resource Exchange
We formulate the interaction between MO and DOs as an optimization problem, and the objective is to effectively utilize the communication and computing resource of the MO and DOs to minimize the time to complete an FL task.
Price-Discrimination Game for Distributed Resource Management in Federated Learning
In vanilla federated learning (FL) such as FedAvg, the parameter server (PS) and multiple distributed clients can form a typical buyer's market, where the number of PS/buyers of FL services is far less than the number of clients/sellers.
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning
To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy.
RL-Based User Association and Resource Allocation for Multi-UAV enabled MEC
In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC), i. e., UAVE is studied, where several UAVs are deployed as flying MEC platform to provide computing resource to ground user equipments (UEs).
