Deep machine unlearning is the problem of'removing'from a trained neural network a subset
of its training set. This problem is very timely and has many applications, including the key …

Federated learning (FL) has recently emerged as a promising distributed machine learning
(ML) paradigm. Practical needs of the" right to be forgotten" and countering data poisoning …

Machine unlearning as a fundamental requirement in Machine-Learning-as-a-Service
(MLaaS) has been extensively studied with increasing concerns about data privacy. It …

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 …

Over the past few years, Machine Learning-as-a-Service (MLaaS) has received a surging
demand for supporting Machine Learning-driven services to offer revolutionized user …

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 …

Machine {\em unlearning} has emerged as a prominent and challenging area of interest,
driven in large part by the rising regulatory demands for industries to delete user data upon …

With the rise of the General Data Protection Regulation (GDPR), user data holders should
guarantee the" individual's right to be forgotten". It means user data holders must completely …

The right to be forgotten, also known as the right to erasure, is the right of individuals to have
their data erased from an entity storing it. The status of this long held notion was legally …

We study federated unlearning, a novel problem to eliminate the impact of specific clients or
data points on the global model learned via federated learning (FL). This problem is driven …