The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …

Federated learning (FL) has experienced a boom in recent years, which is jointly promoted
by the prosperity of machine learning and Artificial Intelligence along with emerging privacy …

Federated learning shows a bright promise as a privacy-preserving collaborative learning
technique. However, prevalent solutions mainly focus on all private data sampled from the …

Abstract Cross-device Federated Learning (FL) is a distributed learning paradigm with
several challenges that differentiate it from traditional distributed learning: variability in the …

The individual and integration use of the Internet of Things (IoT), Information-Centric
Networking (ICN), and Federated Learning (FL) have recently been used in several network …

Secure aggregation is a cryptographic protocol that securely computes the aggregation of its
inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

The standard class-incremental continual learning setting assumes a set of tasks seen one
after the other in a fixed and predefined order. This is not very realistic in federated learning …

With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …

In the past decade, the unmanned aerial vehicles (UAVs) swarm has become a disruptive
force reshaping our lives and work. In particular, advances in artificial intelligence have …