IEEE

Overcoming Client Data Deficiency in Federated Learning by Exploiting Unlabeled Data on the Server

Abstract: Federated Learning (FL) is a distributed machine learning paradigm involving multiple clients to train a server model. In practice, clients often possess limited data and are not always ...
IEEE

Pretraining Client Selection Algorithm Based on a Data Distribution Evaluation Model in Federated Learning

Abstract: Federated Learning (FL) allows task initiators (servers) to utilize data from task participants (clients) to train machine learning models while protecting data privacy. However, in the FL ...
GitHub

client-side-data

This repository contain the sample that proves that the Syncfusion Pivot table is capable of loading and displaying a million records instantly and perform scrolling without any performance lag.