Federated learning (FL) is a new and promising paradigm that allows devices to learn
without sharing data with the centralized server. It is often built on decentralized data where …

Due to the implementation of national security-related laws and regulations, data privacy
protection and ownership issues have attracted much attention, meanwhile, the rise of …

To break data silos and address the challenge of green communication, federated learning
(FL) is widely used at network edges to train deep learning models in mobile edge …

In recent years, 5G/5G-A technology has fast developed and found widespread deployment,
meeting the diverse requirements of application scenarios across various industries. In this …

In quantitative trading, stock prediction plays a crucial role in portfolio optimization as it
directly impacts the actual level of return. However, the trading market is complex, making …

The growth analysis of enterprises is an important basis for predicting the future
development trend of enterprises. For an enterprise itself, the enterprise growth analysis can …

With AI and big data technologies, telecom operators are looking to change the traditional
O&M model from reactive problem handling to proactive prevention and prediction. This …

Along with constructing new power systems and the Energy Internet, much power data is
generated and stored at the edge devices, which may contain customer privacy and be …

The vigorous development of the digital economy brings new opportunities for enterprise
digital transformation. This article proposes a knowledge-sharing-based digital consulting …

To address the three main challenges in Digital Twin Network (DTN), we propose the Elastic
Digital Twin Network Modeling (EDiTNetMdl) fitting in network life cycle to restrain resource …