We develop FedCluster-a novel federated learning framework with improved optimization
efficiency, and investigate its theoretical convergence properties. The FedCluster groups the …

Federated learning (FL) is a machine learning paradigm where a shared central model is
learned across distributed devices while the training data remains on these devices …

Federated learning (FL) aims to train machine learning models in the decentralized system
consisting of an enormous amount of smart edge devices. Federated averaging (FedAvg) …

Federated Learning (FL) enables the multiple participating devices to collaboratively
contribute to a global neural network model while keeping the training data locally. Unlike …

Federated learning is a distributed machine learning paradigm in which a large number of
clients coordinate with a central server to learn a model without sharing their own training …

Federated learning (FL) is a novel machine learning that performs distributed training locally
on devices and aggregating the local models into a global one. The limited network …

Federated learning (FL) is a new machine learning framework which trains a joint model
across a large amount of decentralized computing devices. Existing methods, eg, Federated …

Federated learning aims to jointly learn statistical models over massively distributed remote
devices. In this work, we propose FedDANE, an optimization method that we adapt from …

Federated learning (FL) enables on-device training over distributed networks consisting of a
massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) …

Federated learning enables on-device training over distributed networks consisting of a
massive amount of modern smart devices, such as smartphones and IoT devices. However …