Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

Although federated learning (FL) has recently been proposed for efficient distributed training
and data privacy protection, it still encounters many obstacles. One of these is the naturally …

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized
devices with labeled data in a privacy-preserving manner. However, data with noisy labels …

Federated learning (FL) addresses data privacy concerns by enabling collaborative training
of AI models across distributed data owners. Wide adoption of FL faces the fundamental …

Federated Learning paradigm ensures basic data privacy of local clients through an iterative
aggregation of model parameters. The success of a global model in federated learning …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

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 …

Federated Learning (FL) is a privacy-oriented framework that allows distributed edge
devices to jointly train a shared global model without transmitting their sensed data to …

Training Deep Learning (DL) models require large, high-quality datasets, often assembled
with data from different institutions. Federated Learning (FL) has been emerging as a …