Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …

To build intelligent model learning in conventional architecture, the local data are required to
be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage …

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 …

Although large language models (LLMs) are widely deployed, the data used to train them is
rarely disclosed. Given the incredible scale of this data, up to trillions of tokens, it is all but …

The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …

Language models (LMs) are trained on vast amounts of text data, which may include private
and copyrighted content. Data owners may request the removal of their data from a trained …

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 …

Human Activity Recognition (HAR) has seen remarkable advances in recent years, driven by
the widespread use of wearable devices and the increasing demand for personalized …

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 …

Abstract We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large
models on a diverse collection of data while minimizing the expected cost to remove the …