Scaling up the convolutional neural network (CNN) size (eg, width, depth, etc.) is known to
effectively improve model accuracy. However, the large model size impedes training on …

Personalized federated learning (PFL) aims to train model (s) that can perform well on the
individual edge-devices' data where the edge-devices (clients) are usually IoT devices like …

Institutions in highly regulated domains such as finance and healthcare often have restrictive
rules around data sharing. Federated learning is a distributed learning framework that …

Personalized federated learning (FL) aims to train model (s) that can perform well for
individual clients that are highly data and system heterogeneous. Most work in personalized …

Distributed machine learning paradigms have benefited from the concurrent advancement of
deep learning and the Internet of Things (IoT), among which federated learning is one of the …

Decentralized learning with private data is a central problem in machine learning. We
propose a novel distillation-based decentralized learning technique that allows multiple …

摘要联邦学**作为一种使用分布式训练数据集构建机器学**模型的新兴技术,
可有效解决不同数据用户之间因联合建模而导致的本地数据隐私泄露问题 …

With the modern invention of high-quality sensors and smart chips with high computational
power, smart devices like smartphones and smart wearable devices are becoming primary …

Federated distillation, a new algorithmic paradigm in Federated learning, enables clients to
train different network architectures. In federated distillation, students can learn information …

Blockchain has emerged as a groundbreaking security technology, playing a vital role in
various industries such as banking, the Internet of Things (IoT), healthcare, education, and …