Federated learning (FL) that enables edge devices to collaboratively learn a shared model
while keeping their training data locally has received great attention recently and can protect …

Federated learning (FL) enables data owners to train a global model with shared gradients
while keeping private training data locally. However, recent research demonstrated that the …

Federated learning engages a set of edge devices to collaboratively train a common model
without sharing their local data and has advantage in user privacy over traditional cloud …

Federated learning (FL) requires frequent uploading and updating of model parameters,
which is naturally vulnerable to gradient leakage attacks (GLAs) that reconstruct private …

The Internet of Things (IoT) is penetrating many aspects of our daily life with the proliferation
of artificial intelligence applications. Federated learning (FL) has emerged as a promising …

Conventional federated learning directly averages model weights, which is only possible for
collaboration between models with homogeneous architectures. Sharing prediction instead …

Federated Learning (FL), as an emerging form of distributed machine learning (ML), can
protect participants' private data from being substantially disclosed to cyber adversaries. It …

Federated learning (FL) enables multiple clients to collaboratively train a model with the
coordination of a central server. Although FL improves data privacy via keeping each client's …

Federated learning (FL) empowers distributed clients to collaboratively train a shared
machine learning model through exchanging parameter information. Despite the fact that FL …

Federated learning (FL) is an emerging distributed learning paradigm with default client
privacy because clients can keep sensitive data on their devices and only share local …