In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …

Natural disasters, such as earthquakes, can cause damage to critical infrastructures and
limit access to vital information, making it difficult for disaster response teams to respond …

The unforeseen events of natural disasters often devastate critical infrastructure and disrupt
communication. The use of unmanned aerial vehicles (UAVs) in emergency response …

Heart disease is a leading global cause of death, while federated learning (FL) is an
effective way to predict it. Due to patient privacy concerns and the centralized nature of …

Federated Learning (FL) has emerged as a powerful model for training collaborative
machine learning (ML) models while maintaining the privacy of participants' data. However …

Federated learning (FL) is a distributed learning paradigm that enables large-scale IoT
devices to collaboratively train a shared model while preserving the privacy of local data. To …

Federated Learning has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …

Algorithm unrolling has emerged as a learning-based optimization paradigm that unfolds
truncated iterative algorithms in trainable neural-network optimizers. We introduce …

Abstract Machine learning plays an increasingly important role in supply chain
management. Due to privacy and security concerns, enterprises are reluctant to share their …