Federated unlearning is a promising paradigm for protecting the data ownership of
distributed clients. It allows central servers to remove historical data effects within the …

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 is an approach that ensures privacy in machine learning, but it has its
limitations when it comes to preserving the right to be forgotten. To address this challenge …

With the growing need to comply with privacy regulations and respond to user data deletion
requests, integrating machine unlearning into IoT-based federated learning has become …

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 …

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) …

Blockchained Federated Learning (FL) has been gaining traction for ensuring the integrity
and traceability of FL processes. Blockchained FL involves participants training models …

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

As the right to be forgotten has been legislated worldwide, many studies attempt to design
machine unlearning mechanisms to enable data erasure from a trained model. Existing …