Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-
the-cloud setting to distributed training over edge devices in order to strengthen data …

Federated learning (FL), a popular decentralized and privacy-preserving machine learning
(FL) framework, has received extensive research attention in recent years. The majority of …

Federated Learning has shown great potentials for the distributed data utilization and
privacy protection. Most existing federated learning approaches focus on the supervised …

Federated learning (FL) has emerged as an effective technique to co-training machine
learning models without actually sharing data and leaking privacy. However, most existing …

Data privacy has become an increasingly important concern in real-world big data
applications such as machine learning. To address the problem, federated learning (FL) has …

Federated learning enables multiple clients, such as mobile phones and organizations, to
collaboratively learn a shared model for prediction while protecting local data privacy …

We study a federated learning (FL) framework to effectively train models from scarce and
skewly distributed labeled data. We consider a challenging yet practical scenario: a few data …

Federated learning promises to use the computational power of edge devices while
maintaining user data privacy. Current frameworks, however, typically make the unrealistic …

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized
devices with labeled data in a privacy-preserving manner. However, data with noisy labels …

With the increasing computing power of edge devices, Federated Learning (FL) emerges to
enable model training without privacy concerns. The majority of existing studies assume the …