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 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 recently emerged as a compelling machine learning paradigm,
prioritizing the protection of privacy for training data. The increasing demand to address …

Federated Unlearning (FU) aims to delete specific training data from an ML model trained
using Federated Learning (FL). We introduce QuickDrop, an efficient and original FU …

We explore the problem of selectively forgetting categories from trained CNN classification
models in federated learning (FL). Given that the data used for training cannot be accessed …

Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients
manipulate their updates to affect the global model. Although various methods exist for …

Federated unlearning (FUL) is an emerging distributed machine learning paradigm which
enables the removal or unlearning of specific training data effects from trained Federated …

With the development of trustworthy Federated Learning (FL), the requirement of
implementing right to be forgotten gives rise to the area of Federated Unlearning (FU) …

Federated Learning (FL) enables collaborative training of a Machine Learning (ML) model
across multiple parties, facilitating the preservation of users' and institutions' privacy by …

Federated learning (FL) enables multiple clients to train a machine learning model
collaboratively without exchanging their local data. Federated unlearning is an inverse FL …