分层联邦学习(hierarchical federated learning, HFL) 旨在通过多层架构的协作学习,
同时保护隐私和优化模型性能. 但其效果需依赖于针对参与各方的有效激励机制及应对信息不 …

To avoid the original private data uploading in cloud-edgecomputing, the federated learning
(FL) scheme is recently proposed which enhances the privacy preservation. However, the …

Federated learning is a promising tool in the Internet-of-Things (IoT) domain for training a
machine learning model in a decentralized manner. Specifically, the data owners (eg, IoT …

One of the enabling technologies of Edge Intelligence is the privacy preserving machine
learning paradigm called Federated Learning (FL). However, communication inefficiency …

Federated Learning (FL) allows clients to keep local datasets and train collaboratively by
uploading model gradients, which achieves the goal of learning from fragmented sensitive …

Deep learning has achieved the high-accuracy of state-of-the-art algorithms in long-standing
AI tasks. Due to the obvious privacy issues of deep learning, Google proposes Federal Deep …

The federated learning scheme enhances the privacy preservation through avoiding the
private data uploading in cloud-edge computing. However, the attacks against the uploaded …

Federated learning (FL) has emerged as a prevalent distributed machine learning scheme
that enables collaborative model training without aggregating raw data. Cloud service …

Federated Learning rests on the notion of training a global model distributedly on various
devices. Under this setting, users' devices perform computations on their own data and then …

Federated learning (FL) permits a vast number of connected to construct deep learning
models while keeping their private training data on the device. Rather than uploading the …