The development of artificial intelligence (AI) provides opportunities for the promotion of
deep neural network (DNN)-based applications. However, the large amount of parameters …

Federated learning (FL) is a promising distributed learning framework where distributed
clients collaboratively train a machine learning model coordinated by a server. To tackle the …

Federated learning (FL) has been extensively exploited in the training of machine learning
models to preserve data privacy. In particular, wireless FL enables multiple clients to …

The amount of data generated owing to the rapid development of the Smart Internet of
Things is increasing exponentially. Traditional machine learning can no longer meet the …

Federated learning (FL) is a promising distributed machine learning paradigm to train a
machine learning model without the leakage of local data. However, as the sizes of models …

Federated learning is a promising distributed learning paradigm for protecting data privacy
by delegating learning tasks to local clients and aggregating local models, instead of raw …

The development of artificial intelligence (AI) provides opportunities for the promotion of
deep neural network (DNN)-based applications. However, the large amount of parameters …

Split Federated Learning (SFL) is a distributed machine learning framework which
strategically divides the learning process between a server and clients and collaboratively …

针对零信任边缘计算环境下联邦学习面临的隐私安全及学习效率低等问题,
提出了一种边缘计算中基于多级代理许可区块链的联邦学习模型, 设计多级代理许可区块链构建 …

To solve the straggler problem in federated learning (FL), in this paper we propose an
asynchronous wireless FL scheme where each client keeps local updates and is probabilis …