Aug 11, 2020 · Abstract:Federated learning allows for the training of a model using data on multiple clients without the clients transmitting that raw data ...

Nov 2, 2023 · Supplemental synthetic data can reduce time and cost to build federated learning models that combine the insights of many different ...

Oct 31, 2023 · Federated Learning (FL) is a decentralised approach to training statistical models, where training is performed across multiple clients, producing one global ...

Federated learning allows for the training of a model using data on multiple clients without the clients transmitting that raw data.

Sep 26, 2020 · Abstract. Federated learning allows for the training of a model using data on multiple clients without the clients transmitting that raw ...

Federated Learning (FL) is a decentralised approach to training statistical models, where training is performed across multiple clients, producing one global ...

We propose a new scheme for upstream communication where instead of transmitting the model update, each client learns and transmits a light-weight synthetic ...

higher test accuracy then random masking, a popular compression method used in federated learning. 128. We also show how number of batches of synthetic data(m) ...

May 24, 2023 · In this paper, we propose a hybrid solution, PP-FedGAN, to the asynchronous federated, privacy-preserving training of GANs models.

Aug 11, 2020 · A method for federated learning where instead of transmitting a gradient update back to the server, a small amount of synthetic `data' is ...