Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

Intrusion Detection Systems (IDS) are essential for securing computer networks by
identifying and mitigating potential threats. However, traditional IDS face challenges related …

Federated learning (FL), with the aim of training machine learning models using data and
computational resources on edge devices without sharing raw local data, is essential for …

Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth's
land surface within the scope of Earth observation (EO). Technological advances in capture …

Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training across multiple devices while preserving data …

Heterogeneous data is endemic due to the use of diverse models and settings of devices by
hospitals in the field of medical imaging. However, there are few open-source frameworks …

Federated learning (FL) relies on a central authority to oversee and aggregate model
updates contributed by multiple participating parties in the training process. This …

The integration of AI and ML into energy forecasting is crucial for modern energy
management. Federated Learning (FL) is particularly noteworthy because it enhances data …

Multimodal federated learning (FL) aims to enrich model training in FL settings where clients
are collecting measurements across multiple modalities. However, key challenges to …

Federated Learning (FL) emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …