Federated learning (FL) has recently emerged as a promising distributed machine learning
(ML) paradigm. Practical needs of the" right to be forgotten" and countering data poisoning …

Federated learning (FL) has been widely acknowledged as a promising solution to training
machine learning (ML) model training with privacy preservation. To reduce the traffic …

In Machine Learning, the emergence of the right to be forgotten gave birth to a paradigm
named machine unlearning, which enables data holders to proactively erase their data from …

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 …

Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …

Federated learning (FL) enables large amounts of participants to construct a global learning
model, while storing training data privately at each client device. A fundamental issue in this …

The Right to be Forgotten gives a data owner the right to revoke their data from an entity
storing it. In the context of federated learning, the Right to be Forgotten requires that, in …

Federated learning (FL) is a promising approach for training decentralized data located on
local client devices while improving efficiency and privacy. However, the distribution and …

Federated learning (FL) provides a high efficient decentralized machine learning framework,
where the training data remains distributed at remote clients in a network. Though FL …

Today data is often scattered among billions of resource-constrained edge devices with
security and privacy constraints. Federated Learning (FL) has emerged as a viable solution …