Recently, Machine Unlearning (MU) has gained considerable attention for its potential to
improve AI safety by removing the influence of specific data from trained Machine Learning …

We explore machine unlearning (MU) in the domain of large language models (LLMs),
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …

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

Large Language Models (LLMs) have highlighted the necessity of effective unlearning
mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims …

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

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 …

Personal digital data is a critical asset, and governments worldwide have enforced laws and
regulations to protect data privacy. Data users have been endowed with the right to be …

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

Federated Learning (FL) has evolved as a powerful tool for collaborative model training
across multiple entities, ensuring data privacy in sensitive sectors such as healthcare and …