Federated Learning (FL) is emerging as a popular, promising decentralized learning
framework that enables collaborative training among clients, with no need to share private …

Federated Learning (FL) is designed to protect the data privacy of each client during the
training process by transmitting only models instead of the original data. However, the …

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

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Federated unlearning has emerged very recently as an attempt to realize “the right to be
forgotten” in the context of federated learning. While the current literature is making efforts on …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

In the realm of machine learning (ML) systems featuring client-host connections, the
enhancement of privacy security can be effectively achieved through federated learning (FL) …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

Federated Learning (FL) is a 1 decentralized machine learning (DML) technique. It was
introduced by Google research team in 2017, where several clients can train a model …

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