Federated Learning (FL) is a newly emerged decentralized machine learning (ML)
framework that combines on-device local training with server-based model synchronization …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

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 …

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) supports training models on geographically distributed devices.
However, traditional FL systems adopt a centralized synchronous strategy, putting high …

Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …

Federated Learning (FL) is a promising distributed learning paradigm and has gained recent
attention from both academia and industry. One challenge in FL is that when local data …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …

Federated Learning (FL), an emerging distributed machine learning (ML), empowers a large
number of embedded devices (eg, phones and cameras) and a server to jointly train a …

Federated Learning (FL) is a type of distributed machine learning, which avoids sharing
privacy and sensitive data with a central server. Despite the advances in FL, current …