Federated learning (FL) provides a distributed learning framework for multiple participants to
collaborate learning without sharing raw data. In many practical FL scenarios, participants …

Federated learning (FL) supports training models on geographically distributed devices.
However, traditional FL systems adopt a centralized synchronous strategy, putting high …

Federated learning (FL) enables collaboratively training a model while keeping the training
data decentralized and private. However, one significant impediment to training a model …

Federated learning (FL) enables distributed mobile devices to collaboratively learn a shared
model without exposing their raw data. However, heterogeneous devices usually have …

Federated Learning (FL) is an emerging paradigm that enables distributed users to
collaboratively and iteratively train machine learning models without sharing their private …

When the available hardware cannot meet the memory and compute requirements to
efficiently train high performing machine learning models, a compromise in either the …

Personalized federated learning considers learning models unique to each client in a
heterogeneous network. The resulting client-specific models have been purported to …

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) involves training a model over massive distributed devices, while
keeping the training data localized. This form of collaborative learning exposes new …

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …