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Federated Learning Using Three-Operator ADMM
We propose FedTOP-ADMM, which generalizes FedADMM and is based on a three-operator ADMM-type technique that exploits a smooth cost function on the edge server to learn a global model parallel to the edge devices.
On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations
Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage.
Simultaneous Wireless Information and Power Transfer for Federated Learning
We investigate the trade-off between the number of communication rounds and communication round time while harvesting energy to compensate the energy expenditure.
Dynamic Clustering in Federated Learning
To overcome these limitations, in this paper, we propose a three-phased data clustering algorithm, namely: generative adversarial network-based clustering, cluster calibration, and cluster division.
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