Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Federated learning (FL) enables participants to collaboratively construct a global machine
learning model without sharing their local training data to the remote server. In FL systems …

Defining and evaluating data quality can be a complex task as it varies depending on the
specific purpose for which the data is intended. To effectively assess data quality, it is …

Federated Learning (FL) enables a large number of data owners (aka FL clients) to jointly
train a machine learning model without disclosing private local data. The importance of local …

As the number of clients for federated learning (FL) has expanded to the billion level, a new
research branch named secure federated submodel learning (SFSL) has emerged. In SFSL …

Federated learning (FL) enables distributed participants to collaboratively train a machine
learning model without accessing to their local data. In FL systems, the selection of training …

In Federated Learning (FL) systems, clients share updates derived from their local data with
the server while maintaining privacy. The server aggregates these updates to refine the …

Federated learning coordinates distributed data sets to train models, which brings the
significant impact of data selection on model performance. Personalized differential privacy …

This paper presents a comprehensive exploration of data quality terminology, revealing a
significant lack of standardisation in the field. The goal of this work was to conduct a …

Data augmentation, as a common technique in deep learning training, is primarily used to
mitigate overfitting problems, especially with small-scale datasets. However, it is difficult for …