Training a deep neural network requires substantial data and intensive computing
resources. Unaffordable price holds back many potential applications of deep learning …

Federated learning (FL), as a distributed machine learning setting, has received
considerable attention in recent years. To alleviate privacy concerns, FL essentially …

Federated Learning (FL) is suitable for the application scenarios of distributed edge
collaboration of the Internet of Things (IoT). It can provide data security and privacy, which is …

Federated learning is an improved distributed machine learning approach for privacy
preservation. All clients collaboratively train the model using on-device data, and the …

Federated learning faces many security and privacy issues. Among them, poisoning attacks
can significantly impact global models, and malicious attackers can prevent global models …

As promising privacy-preserving machine learning technology, federated learning enables
multiple clients to train the joint global model via sharing model parameters. However …

Machine learning (ML) algorithms are essential components in autonomous driving. In most
existing connected and autonomous vehicles (CAVs), a large amount of driving data …

Intelligent transportation systems, especially Autonomous Vehicles (AVs), are emerging as a
paradigm with the potential to change modern society. However, with this, there is a strong …

As a novel distributed machine learning scheme, federated learning (FL) efficiently realizes
the collaborative training of models by global participants while also protecting their data …

Federated learning is a novel distributed learning framework, which enables thousands of
participants to collaboratively construct a deep learning model. In order to protect …