Deep learning models have raised privacy and security concerns due to their reliance on
large datasets on central servers. As the number of Internet of Things (IoT) devices …

While centralized servers pose a risk of being a single point of failure, decentralized
approaches like blockchain offer a compelling solution by implementing a consensus …

Federated learning (FL) is an emerging promising privacy-preserving machine learning
paradigm and has raised more and more attention from researchers and developers. FL …

Emerging technologies and applications including the Internet of Things (IoT), social
networking, and crowd-sourcing generate large amounts of data at the network edge …

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …

Federated learning (FL) is a distributed machine learning (ML) technique that enables
collaborative training in which devices perform learning using a local dataset while …

Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning,
has garnered significant interest from scholars and engineers across both academic and …

Federated learning (FL) is a promising decentralized deep learning technology, which
allows users to update models cooperatively without sharing their data. FL is reshaping …

Federated learning (FL) is a distributed machine learning approach that protects user data
privacy by training models locally on clients and aggregating them on a parameter server …

Federated Learning (FL) is a distributed machine learning paradigm that trains models
across multiple devices without exchanging users' data, thereby providing stronger data …