The metaverse is a nascent concept that envisions a virtual universe, a collaborative space
where individuals can interact, create, and participate in a wide range of activities. Privacy in …

This review highlights the efficacy of combining federated learning (FL) and transfer learning
(TL) for cancer detection via image analysis. By integrating these techniques, research has …

Abstract Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a
reputation for not only building Machine Learning (ML) models that rely on distributed …

Federated learning is a promising approach in the Internet of Things (IoT) that enables
collaborative and distributed machine learning among massive IoT devices without sharing …

Federated learning is a decentralized machine learning approach where clients train
models locally and share model updates to develop a global model. This enables low …

Federated learning systems encounter various security risks, including backdoor, inference
and adversarial attacks. Backdoor attacks within this context generally require careful trigger …

When personalized federated learning (FL) meets large foundation models, new challenges
arise from various limitations in resources. In addition to typical limitations such as data …

Recently, researchers have successfully employed Graph Neural Networks (GNNs) to build
enhanced recommender systems due to their capability to learn patterns from the interaction …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Due to the increase in data regulations amid rising privacy concerns, the machine learning
(ML) community has proposed a novel distributed training paradigm called federated …