Federated learning has emerged as an important distributed learning paradigm, which
normally involves collaborative updating with others and local updating on private data …

Abstract Recent developments in Artificial Intelligence techniques have enabled their
successful application across a spectrum of commercial and industrial settings. However …

Federated Learning (FL) has emerged as a promising distributed learning paradigm that
enables multiple clients to learn a global model collaboratively without sharing their private …

The pervasiveness of AI in society has made machine learning (ML) an invaluable tool for
mobile and internet-of-things (IoT) devices. While the aggregate amount of data yielded by …

Recently, federated learning has received increasing attention from academe and industry,
since it makes training models with decentralized data possible. However, most existing …

Federated learning learns from scattered data by fusing collaborative models from local
nodes. However, conventional coordinate-based model averaging by FedAvg ignored the …

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 …

The uneven distribution of local data across different edge devices (clients) results in slow
model training and accuracy reduction in federated learning. Naive federated learning (FL) …

Statistical heterogeneity of data present at client devices in a federated learning (FL) system
renders the training of a global model in such systems difficult. Particularly challenging are …

In Federated Learning (FL), model aggregation is pivotal. It involves a global server
iteratively aggregating client local trained models in successive rounds without accessing …