Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Federated learning (FL) leverages the private data and computing power of multiple clients
to collaboratively train a global model. Many existing FL algorithms over wireless networks …

In this paper, we study a new latency optimization problem for blockchain-based federated
learning (BFL) in multi-server edge computing. In this system model, distributed mobile …

Data generated at the network edge can be processed locally by leveraging the paradigm of
Edge Computing (EC). Aided by EC, Federated Learning (FL) has been becoming a …

The conventional federated learning (FL) framework usually assumes synchronous
reception and fusion of all the local models at the central aggregator and synchronous …

Existing device scheduling works in wireless federated learning (FL) mainly focused on
selecting the devices with maximum gradient norm or loss function and require all devices to …

The existing federated learning framework is based on the centralized model coordinator,
which still faces serious security challenges such as device differentiated computing power …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …