Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the
emerging Internet of Things (IoT) has gained a lot of attention from the government …

The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning
algorithm for both network and application management. However, given the presence of …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …