Federated learning (FL) offers an effective learning architecture to protect data privacy in a
distributed manner. However, the inevitable network asynchrony, overdependence on a …

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

Federated learning (FL) has received significant attention from both academia and industry,
as an emerging paradigm for building machine learning models in a communication-efficient …

Federated learning (FL) allows a server to learn a machine learning (ML) model across
multiple decentralized clients that privately store their own training data. In contrast with …

Federated learning (FL) systems are susceptible to attacks from malicious actors who might
attempt to corrupt the training model through various poisoning attacks. FL also poses new …

Federated Learning (FL), while a breakthrough in decentralized machine learning, contends
with significant challenges such as limited data availability and the variability of …

Federated learning (FL) is a pivotal catalyst for enabling large-scale privacy-preserving
distributed machine learning (ML). By eliminating the need for local raw dataset sharing, FL …

In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …

With more regulations tackling the protection of users' privacy-sensitive data in recent years,
access to such data has become increasingly restricted. A new decentralized training …

Federated learning (FL) enables the training of models among distributed clients without
compromising the privacy of training datasets, while the invisibility of clients datasets and the …