Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

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 …

Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …

Federated learning (FL) allows edge devices to collectively learn a model without directly
sharing data within each device, thus preserving privacy and eliminating the need to store …

Federated learning (FL) is an attractive distributed learning paradigm supporting real-time
continuous learning and client privacy by default. In most FL approaches, all edge clients …

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of
the world's web traffic, making a great data source for various machine learning (ML) …

The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …

Applying federated learning (FL) on Internet of Things (IoT) devices is necessitated by the
large volumes of data they produce and growing concerns of data privacy. However, there …