With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous
networks, mobile broadband users are generating massive volumes of data that undergo …

Due to the strong analytical ability of big data, deep learning has been widely applied to
model on the collected data in industrial Internet of Things (IoT). However, for privacy issues …

Federated learning can combine a large number of scattered user groups and train models
collaboratively without uploading data sets, so as to avoid the server collecting user …

With federated learning, one of the most notable features is that it can update global model
parameter without using the users' local data. However, various security and privacy …

To improve data availability and durability, cloud users would like to store multiple copies of
their original files at servers. The multi-copy auditing technique is proposed to provide users …

Due to the complexity of the data environment, many organizations prefer to train deep
learning models together by sharing training sets. However, this process is always …

Fog computing is an emerging computing paradigm which expands cloud-based computing
services near the network edge. With this new computing paradigm, new challenges arise in …

Cloud computing has strong computing power and huge storage space. Machine learning
algorithm, combining with cloud computing, makes the processing of large-scale data …

In the era of big data, there are several insuperable research challenges in establishing
Electronic Medical Record (EMR) for updating massive data with traditional methods. It is an …

With the development of machine learning, it is popular that mobile users can submit
individual symptoms at any time anywhere for medical diagnosis. Edge computing is …