Federated Learning (FL) is a distributed learning paradigm that enables different parties to
train a model together for high quality and strong privacy protection. In this scenario …

As a new distributed machine learning framework, Federated Learning (FL) effectively
solves the problems of data silo and privacy protection in the field of artificial intelligence …

Federated learning (FL) allows multiple clients to train deep learning models collaboratively
while protecting sensitive local datasets. However, FL has been highly susceptible to …

Federated Learning (FL) as a distributed learning paradigm that aggregates information
from diverse clients to train a shared global model, has demonstrated great success …

Federated learning (FL) emerged as a promising learning paradigm to enable a multitude of
participants to construct a joint ML model without exposing their private training data …

Federated Learning (FL) enhances decentralized machine learning by safeguarding data
privacy, reducing communication costs, and improving model performance with diverse data …

Federated learning is a secure machine learning technology proposed to protect data
privacy and security in machine learning model training. However, recent studies show that …

Federated Learning (FL) is a distributed machine learning paradigm that allows multiple
clients to train a global model collaboratively without sharing their local training data. Due to …

The Poisoning Membership Inference Attack (PMIA) is a newly emerging privacy attack that
poses a significant threat to federated learning (FL). An adversary conducts data poisoning …

Federated learning (FL), as a distributed machine learning setting, has received
considerable attention in recent years. To alleviate privacy concerns, FL essentially …