Federated learning involves training machine learning models over devices or data silos,
such as edge processors or data warehouses, while keeping the data local. Training in …

Federated Learning (FL) has emerged as an efficient distributed model training framework
that enables multiple clients cooperatively to train a global model without exposing their …

Delayed Gradient Averaging (DGA) has gained massive attention for improving the training
efficiency of Federated Learning (FL) at edge networks, by allowing local computation in …

Traditional Federated Learning (FL) is a promising paradigm that enables massive edge
clients to collaboratively train deep neural network (DNN) models without exposing raw data …

Non-independent and identical distribution (Non-IID) data and model heterogeneity pose a
great challenge for federated learning in cloud-based and edge-based systems. They are …

Federated Averaging (FedAvg) and its variants are prevalent optimization algorithms
adopted in Federated Learning (FL) as they show good model convergence. However, such …

In this work, we investigate the challenging problem of on-demand federated learning (FL)
over heterogeneous edge devices with diverse resource constraints. We propose a cost …

Federated learning (FL) has received considerable attention with the development of mobile
internet technology, which is an emerging framework to train a deep learning model from …

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 …

Recently, edge AI has been launched to mine and discover valuable knowledge at network
edge. Federated Learning, as an emerging technique for edge AI, has been widely …