Federated learning (FL) and split learning (SL) are two emerging collaborative learning
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …

Federated learning is a promising learning paradigm that allows collaborative training of
models across multiple data owners without sharing their raw datasets. To enhance privacy …

Split learning (SL) is a popular distributed machine learning (ML) method used to enable
ML. It divides a neural network based model into subnetworks. Then, it separately trains the …

In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of
Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data …

Recent years have witnessed a spurt of progress in federated learning, which can
coordinate multi-participation model training while protecting the data privacy of participants …

Accurate load forecasting is crucial for energy management, infrastructure planning, and
demand-supply balancing. Smart meter data availability has led to the demand for sensor …

摘要针对传统遥感图像处理中的时间成本和人工成本高, 效率低等问题, 以提高遥感高光谱图像
分类中的处理速度, 精度, 降低参数量为目标, 提出改进的2DCNN 模型En⁃ De⁃ 2CP⁃ …

Distributed learning and inference algorithms have become indispensable for IoT systems,
offering benefits such as workload alleviation, data privacy preservation, and reduced …