The combination of artificial intelligence and mobile edge computing (MEC) is considered as
a promising evolution path of the future wireless networks. As a model-level coordination …

The emerging Federated Learning (FL) permits all workers (eg, mobile devices) to
cooperatively train a model using their local data at the network edge. In order to avoid the …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …

Federated learning can achieve distributed machine learning without sharing privacy and
sensitive data of end devices. However, high concurrent access to cloud servers increases …

Federated learning is a model-level aggregation learning paradigm, which can generate
high quality models without collecting the local private data of users. As a distributed …

Federated learning is a distributed machine learning technology that can protect users' data
privacy, so it has attracted more and more attention in the industry and academia …

Federated Learning is a new learning scheme for collaborative training a shared prediction
model while keeping data locally on participating devices. In this paper, we study a new …

Federated learning (FL) is a distributed machine learning (ML). Distributed clients train
locally and exclusively need to upload the model parameters to learn the global model …

Recently, a novel machine learning technique, federated learning, attracts ever-increasing
interests from academia to industry. The main idea of federated learning is to collaboratively …

Federated learning is a promising technique to implement network intelligence for the sixth
generation (6G) communication systems. However, the collected data in wireless networks …