Federated learning (FL) enables wireless terminals to collaboratively learn a shared
parameter model while keeping all the training data on devices per se. Parameter sharing …

It is predicted that 50 billion devices will be connected to the Internet by 2020, and the
number of mobile-connected devices will exceed 11.5 billion by 2019. These growth …

The full deployment of sixth-generation (6G) networks is inextricably connected with a
holistic network redesign able to deal with various emerging challenges, such as integration …

With the rapid development of smart terminals and infrastructures, as well as diversified
applications (eg, virtual and augmented reality, remote surgery and holographic projection) …

Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …

Federated learning is a promising paradigm that allows multiple clients to collaboratively
train a model without sharing the local data. However, the presence of heterogeneous …

Federated learning (FL) enables collaborative model training without centralizing data.
However, the traditional FL framework is cloud-based and suffers from high communication …

Federated learning (FL) is emerging as a new paradigm for training a machine learning
model in cooperative networks. The model parameters are optimized collectively by large …

It is envisioned that data-driven distributed learning approaches such as federated learning
(FL) will be a key enabler for 6G wireless networks. However, the deployment of FL over …

Federated Learning (FL) is a type of Machine Learning (ML) technique in which only learned
models are stored on a server to sustain data security. The approach does not gather server …