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 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), introduced in 2017, facilitates collaborative learning between non-
trusting parties with no need for the parties to explicitly share their data among themselves …

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 is a promising privacy-preserving paradigm for distributed machine
learning. In this context, there is sometimes a need for a specialized process called machine …

In recent years, Federated Unlearning (FU) has gained attention for addressing the removal
of a client's influence from the global model in Federated Learning (FL) systems, thereby …

Federated learning (FL) has recently attracted much attention due to its advantages for data
privacy. But every coin has two sides: protecting users' data (not requiring users to send their …

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

Federated learning (FL) has emerged as a privacy-aware collaborative learning paradigm
where participants jointly train a powerful model without sharing their private data. One …