Advances in the new generation of Internet of Things (IoT) technology are propelling the
growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption …

Background The collection, storage, and analysis of large data sets are relevant in many
sectors. Especially in the medical field, the processing of patient data promises great …

Federated learning (FL) enables users to train the global model cooperatively without
exposing their private data across the engaged parties, which is widely used in privacy …

With growing demand for privacy-preserving reinforcement learning (RL) applications,
federated RL (FRL) has emerged as a potential solution. However, existing FRL methods …

The popularity of emerging consumer electronics, such as Mobile phones, PADs, and
various smart home appliances, brings unprecedented convenience to people. Currently …

Differential privacy is an essential tool in federated learning privacy preservation. However,
existing differential privacy-preserving techniques introduce excessive noise perturbations …

Federerad inlärning (eng. Federated Learning, FL) är ett nytt paradigm inom
maskininlärning (ML) som möjliggör att flera fysiska enheter samarbetar för att gemensamt …

Aim This study presents a cutting-edge survey on privacy issues, security attacks,
countermeasures and open problems in FL. Methodology The Preferred Reporting Items for …

The amount of data generated owing to the rapid development of the Smart Internet of
Things is increasing exponentially. Traditional machine learning can no longer meet the …

Federated learning (FL) as a novel paradigm in Artificial Intelligence (AI), ensures enhanced
privacy by eliminating data centralization and brings learning directly to the edge of the …