Federated Learning (FL) is a privacy-preserving distributed machine learning paradigm.
Nonetheless, the substantial distribution shifts among clients pose a considerable challenge …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

The right to be forgotten (RTBF) is a concept that pertains to an individual's right to request
the removal or deletion of their personal information when it is no longer necessary …

Vertical federated learning (VFL) revolutionizes privacy-preserved collaboration for small
businesses that have distinct but complementary feature sets. However, as the scope of VFL …

Federated learning (FL) is a machine learning paradigm for data silos and privacy
protection, which aims to organize multiple clients for training global machine learning …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

Federated learning (FL) facilitates collaboration between a group of clients who seek to train
a common machine learning model without directly sharing their local data. Although there …

The Right to be Forgotten gives a data owner the right to revoke their data from an entity
storing it. In the context of federated learning, the Right to be Forgotten requires that, in …

Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm
that allows collaborative learning with data privacy protection, personalized FL (pFL) has …

The increasing concerns regarding the privacy of machine learning models have catalyzed
the exploration of machine unlearning, ie, a process that removes the influence of training …