Federated learning (FL) is a promising way to use the computing power of mobile devices
while maintaining the privacy of users. Current work in FL, however, makes the unrealistic …

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

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 allows training machine learning models by using the computation and
private data resources of many distributed clients such as smartphones and IoT devices …

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 …

As a mechanism for devices to update a global model without sharing data, federated
learning bridges the tension between the need for data and respect for privacy. However …

In this work, we propose GPT-FL, a generative pre-trained model-assisted federated
learning (FL) framework. At its core, GPT-FL leverages generative pre-trained models to …

Federated learning (FL) is a distributed machine learning architecture that leverages a large
number of workers to jointly learn a model with decentralized data. FL has received …

Federated Learning (FL) aims to generate a global shared model via collaborating
decentralized clients with privacy considerations. Unlike standard distributed optimization …