Federated learning (FL) is an emerging machine learning paradigm for preserving data
privacy. However, diverse client hardware often has varying computation resources. Such …

Federated learning (FL) enables distributed optimization of machine learning models while
protecting privacy by independently training local models on each client and then …

The ever-growing data scale and increasingly strict privacy restraint have recently drawn
extensive attention to federated learning (FL) as a multi-party machine learning paradigm for …

Federated Learning is an emerging privacy-preserving distributed machine learning
approach to building a shared model by performing distributed training locally on …

Deep learning has shown incredible potential across a vast array of tasks and
accompanying this growth has been an insatiable appetite for data. However, a large …

Federated Learning (FL) enables the training of Deep Learning models without centrally
collecting possibly sensitive raw data. This paves the way for stronger privacy guarantees …

Learning a global model by abstracting the knowledge, distributed across multiple clients,
without aggregating the raw data is the primary goal of Federated Learning (FL). Typically …

Abstract The Internet-of-Things (IoT) generates vast quantities of data. Much of this data is
attributable to human activities and behavior. Collecting personal data and executing …

Data privacy is becoming increasingly important as data becomes more valuable, as
evidenced by the enactment of right-to-be-forgotten laws and regulations. However, in a …

Federated learning (FL) has emerged as a promising solution to address the challenges
posed by data silos and the need for global data fusion. It offers a distributed machine …