The explosively growth of mobile applications imposes much burden on the current
computing networks. Wireless Computing Power Network (WCPN), as an emerging …

The emerging blockchained federated learning, known for its security properties such as
decentralization, immutability and traceability, is evolving into an important direction of next …

In the 6G era, the proliferation of data and data-intensive applications poses unprecedented
challenges on the current communication and computing networks. The collaboration …

Blockchain technology has been extensively studied to enable distributed and tamper-proof
data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) …

The fast proliferation of edge computing devices brings an increasing growth of data, which
directly promotes machine learning (ML) technology development. However, privacy issues …

Federated Learning (FL) has emerged as a privacy-preserving distributed Machine Learning
paradigm, which collaboratively trains a shared global model across a number of end …

Due to the distributed characteristics of federated learning (FL), the vulnerability of the global
model and the coordination of devices are the main obstacle. As a promising solution of …

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …

Federated learning (FL) is a distributed machine learning (ML) technique that enables
collaborative training in which devices perform learning using a local dataset while …

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving,
Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI …