In recent years, the notion of “the right to be forgotten”(RTBF) has become a crucial aspect of
data privacy for digital trust and AI safety, requiring the provision of mechanisms that support …

Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …

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

The evolution of privacy-preserving Federated Learning (FL) has led to an increasing
demand for implementing the right to be forgotten. The implementation of selective forgetting …

AI Safety is an emerging area of critical importance to the safe adoption and deployment of
AI systems. With the rapid proliferation of AI and especially with the recent advancement of …

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 …

Federated unlearning (FUL) is a promising solution for removing negative influences from
the global model. However, ensuring the reliability of local models in FL systems remains …

Federated learning (FL) offers a credible solution for distributed data trading since it could
train machine learning models in a distributed manner thereby enhancing data privacy …

In recent years, deep learning methods based on a large amount of data have achieved
substantial success in numerous fields. However, with increases in regulations for protecting …

Federated Learning (FL) is essential for efficient data exchange in Internet of Things (IoT)
environments, as it trains Machine Learning (ML) models locally and shares only model …