Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

Abstract Asynchronous Federated Learning (AFL) has been introduced to improve the
efficiency of FL by reducing the latency of Machine Learning (ML) model aggregation …

Federated learning (FL) is a promising paradigm that enables collaboratively learning a
shared model across massive clients while keeping the training data locally. However, for …

Federated learning (FL) is an emerging distributed machine learning paradigm that protects
privacy and tackles the problem of isolated data islands. At present, there are two main …

Federated Learning (FL) has gained increasing interest in recent years as a distributed on-
device learning paradigm. However, multiple challenges remain to be addressed for …

Federated Learning (FL) has emerged as a promising methodology for collaboratively
training machine learning models on decentralized devices. Notwithstanding, the effective …

To fully exploit enormous data generated by intelligent devices in edge computing, edge
federated learning (EFL) is envisioned as a promising solution. The distributed collaborative …

Federated Continual Learning (FCL) has emerged as a promising paradigm that combines
Federated Learning (FL) and Continual Learning (CL). To achieve good model accuracy …

To solve the data island problem, federated learning (FL) provides a solution paradigm
where each client sends the model parameters but not the data to a server for model …

The analysis of distributed techniques is often focused upon their efficiency, without
considering their robustness (or lack thereof). Such a consideration is particularly important …