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 …

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 …

Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities that
increasingly influence various aspects of our daily lives, constantly defining the new …

Machine unlearning is motivated by desire for data autonomy: a person can request to have
their data's influence removed from deployed models, and those models should be updated …

Federated Unlearning (FU) is gaining prominence for its capacity to eliminate influences of
Federated Learning (FL) users' data from trained global FL models. A straightforward FU …

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 …

Language models (LMs) are susceptible to" memorizing" training data, including a large
amount of private or copyright-protected content. To safeguard the right to be forgotten …