In the future era of intelligent networks, communication technology and network architecture
need to be further developed to provide users with high-quality services. The Space-Air …

Traffic patterns of urban road intersections are important in traffic monitoring and accident
prediction, thus play crucial roles in urban traffic management. Although real-time traffic …

Federated learning provides a way to achieve joint model training while keeping the data of
every party stored locally, and it protects the data privacy of all participants in cooperative …

Federated learning (FL) allows clients at the edge to learn a shared global model without
disclosing their private data. However, FL is susceptible to poisoning attacks, wherein an …

Federated learning (FL) allows multiple clients to train deep learning models collaboratively
while protecting sensitive local datasets. However, FL has been highly susceptible to …

Targeted model poisoning attacks pose a significant threat to federated learning systems.
Recent studies show that edge-case targeted attacks, which target a small fraction of the …

Federated Learning enables devices to collaboratively learn a shared prediction model
while keeping all the training data on the local device and promoting clients' privacy. Vanilla …

Blockchain-based decentralized federated learning (BCFL) protects data privacy and avoids
the single point of failure, which has become a key technology in the Intelligent Internet of …

Federated Learning (FL) is a decentralized machine learning approach developed to ensure
that training data remains on personal devices, preserving data privacy. However, the …

Federated Learning (FL), a novel distributed machine learning framework, made it possible
to model collaboratively without risking participants' privacy. All components of FL, including …