Human Activity Recognition (HAR) has seen remarkable advances in recent years, driven by
the widespread use of wearable devices and the increasing demand for personalized …

Human activity recognition (HAR) using wearable sensors enables continuous monitoring
for healthcare applications. However, the conventional centralised training of deep learning …

The rapid proliferation of Internet of Things (IoT) devices and the transition to distributed
computing environments necessitate advanced intrusion detection systems (IDS) to …

Cellular traffic forecasting is an essential task that enables network operators to perform
resource allocation and anomaly mitigation in fast-paced modern environments. However …

Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip
learning in embedded devices and emerged as an energy-efficient alternative to …

With the advancement of neuromorphic chips, implementing Federated Learning (FL) with
Spiking Neural Networks (SNNs) potentially offers a more energy-efficient schema for …

Spiking Neural Networks (SNNs), which offer exceptional energy efficiency for inference,
and Federated Learning (FL), which offers privacy-preserving distributed training, is a rising …

In this chapter, the authors proposed an hybrid neuromorphic federated learning framework
that synergizes the computational efficiency of spiking neural networks with the dynamic …