Federated Learning (FL) advocates training a global model using a large-scale of distributed
devices such that the collaborative model training can be benefited from the rich local …

Federated learning (FL) is a promising distributed learning solution that only exchanges
model parameters without revealing raw data. However, the centralized architecture of FL is …

Federated learning (FL) is a new distributed machine learning framework that emerged in
recent years, which can protect the participants' data privacy to a certain extent without …

Federated learning is a decentralized machine learning paradigm that allows multiple
clients to collaborate by leveraging local computational power and the model's transmission …

Over the last few years, end devices' rapid proliferation and development have placed
obstacles on existing networks. New technologies have prompted attention to developing …

Federated learning (FL) is promising in supporting collaborative learning applications that
involve large datasets, massively distributed data owners and unreliable network …

The 14th Five-Year Plan proposes the objective of expediting digital development and
constructing a digital China, aiming to minimize the reliance on physical travel while …

Recently, Blockchain-Enabled Federated Learning (BCFL), an innovative approach that
combines the advantages of Federated Learning and Blockchain technology, is receiving …

Federated learning is an important distributed model training technique in Internet of Things
(IoT), in which participant selection is a key component that plays a role in improving training …

Federated learning is an emerging privacy-preserving AI technique where clients (ie,
organisations or devices) train models locally and formulate a global model based on the …