Federated learning (FedL) has emerged as a popular technique for distributing model
training over a set of wireless devices, via iterative local updates (at devices) and global …

Federated learning has emerged as a popular technique for distributing machine learning
(ML) model training across the wireless edge. In this paper, we propose two timescale …

Federated learning (FL) has emerged as a key technique for distributed machine learning
(ML). Most literature on FL has focused on ML model training for (i) a single task/model, with …

We propose cooperative edge-assisted dynamic federated learning (CE-FL). CE-FL
introduces a distributed machine learning (ML) architecture, where data collection is carried …

Federated learning (FL) is a technique for distributed machine learning (ML), in which edge
devices carry out local model training on their individual datasets. In traditional FL …

Federated learning has emerged recently as a promising solution for distributing machine
learning tasks through modern networks of mobile devices. Recent studies have obtained …

Semi-decentralized federated learning blends the conventional device-to-server (D2S)
interaction structure of federated model training with localized device-to-device (D2D) …

The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …

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 …

Despite the recent advancements achieved by federated learning (FL), its real-world
deployment is significantly impeded by the heterogeneous learning environment …