Federated learning (FL) is a scheme in which several consumers work collectively to unravel
machine learning (ML) problems, with a dominant collector synchronizing the procedure …

The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the
Edge AI concept to provide intelligent solutions close to the end-user environment, for …

Healthcare data are distributed and confidential, making it difficult to use centralized
automatic diagnostic techniques. For example, different hospitals hold the electronic health …

Federated learning (FL) is a privacy-preserving collective machine learning paradigm.
Vertical federated learning (VFL) deals with the case where participants share the same …

In the realm of federated learning (FL), the exchange of model data may inadvertently
expose sensitive information of participants, leading to significant privacy concerns. Existing …

With strict protections and regulations of data privacy and security, conventional machine
learning based on centralized datasets is confronted with significant challenges, making …