Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …

Federated learning enables multiple clients to collaboratively learn a global model by
periodically aggregating the clients' models without transferring the local data. However, due …

Existing federated learning paradigms usually extensively exchange distributed models at a
central solver to achieve a more powerful model. However, this would incur severe …

Is it possible to design an universal API for federated learning using which an ad-hoc group
of data-holders (agents) collaborate with each other and perform federated learning? Such …

Federated Learning (FL) enables the training of Deep Learning models without centrally
collecting possibly sensitive raw data. This paves the way for stronger privacy guarantees …

Federated Distillation (FD) extends classic Federated Learning (FL) to a more general
training framework that enables model-heterogeneous collaborative learning by Knowledge …

Knowledge sharing and model personalization are essential components to tackle the non-
IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1) …

Traditionally, federated learning (FL) aims to train a single global model while
collaboratively using multiple clients and a server. Two natural challenges that FL algorithms …

Federated learning aims to collaboratively train a strong global model by accessing users'
locally trained models but not their own data. A crucial step is therefore to aggregate local …

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 …