Federated machine unlearning (FMU) aims to remove the influence of a specified subset of training data upon request from a trained federated learning model. Despite achieving …
With evolving data regulations, machine unlearning (MU) has become an important tool for fostering trust and safety in today's AI models. However, existing MU methods focusing on …
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 …
P Wang, Z Wei, H Qi, S Wan, Y Xiao… - IEEE Journal on …, 2023 - ieeexplore.ieee.org
Federated Learning (FL), which has been employed to train machine learning models on the data with a distributed manner, could enhance the immersive user experience for the human …
The recent advances in diffusion models (DMs) have revolutionized the generation of complex and diverse images. However, these models also introduce potential safety …
With the development of trustworthy Federated Learning (FL), the requirement of implementing right to be forgotten gives rise to the area of Federated Unlearning (FU) …
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 …
R Jin, M Chen, Q Zhang, X Li - arXiv preprint arXiv:2306.02216, 2023 - arxiv.org
Federated learning (FL) is a trending distributed learning framework that enables collaborative model training without data sharing. Machine learning models trained on …
Federated Learning (FL) enables collaborative training of a Machine Learning (ML) model across multiple parties, facilitating the preservation of users' and institutions' privacy by …