The emerging paradigm of federated learning strives to enable collaborative training of
machine learning models on the network edge without centrally aggregating raw data and …

Federated learning enables the privacy-preserving training of neural network models using
real-world data across distributed clients. FedAvg has become the preferred optimizer for …

The emerging paradigm of federated learning (FL) strives to enable collaborative training of
deep models on the network edge without centrally aggregating raw data and hence …

Federated learning is an emerging learning paradigm where multiple clients collaboratively
train a machine learning model in a privacy-preserving manner. Personalized federated …

Federated Learning enables visual models to be trained in a privacy-preserving way using
real-world data from mobile devices. Given their distributed nature, the statistics of the data …

We consider the problem of Federated Learning (FL) where numerous decentralized
computational nodes collaborate with each other to train a centralized machine learning …

Federated learning (FL) has recently attracted increasing attention from academia and
industry, with the ultimate goal of achieving collaborative training under privacy and …

Federated learning enables resource-constrained edge compute devices, such as mobile
phones and IoT devices, to learn a shared model for prediction, while keeping the training …

Federated learning is a decentralized topology of deep learning, that trains a shared model
through data distributed among each client (like mobile phones, wearable devices), in order …

Many image and vision applications require a large amount of data for model training.
Collecting all such data at a central location can be challenging due to data privacy and …