The heterogeneity of data distributions among clients (non-IID) has been identified as one of
the key challenges in federated learning. In the local training phase, each client model …

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

Federated learning (FL) enables multiple clients to collaboratively train a global model while
keeping local data decentralized. Data heterogeneity (non-IID) across clients has imposed …

Abstract Knowledge distillation has caught a lot of attention in Federated Learning (FL)
recently. It has the advantage for FL to train on heterogeneous clients which have different …

Federated learning, as one enabling technology of edge intelligence, has gained substantial
attention due to its efficacy in training deep learning models without data privacy and …

Federated Learning (FL) is a distributed learning approach that allows each client to retain
its original data locally and share only the parameters of the local updates with the server …

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

As the development of the Internet of Things (IoT) continues, Federated Learning (FL) is
gaining popularity as a distributed machine learning framework that does not compromise …

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 is a distributed machine learning paradigm where the goal is to
collaboratively train a high quality global model while private training data remains local …