Integrating artificial intelligence with food category recognition has been a field of interest for
research for the past few decades. It is potentially one of the next steps in revolutionizing …

This study introduces a blockchain-based lightweight encryption strategy with federated
learning to address the scalability and trust concerns of electronic health records (EHR) …

Federated learning (FL) FL has emerged as a promising privacy-preserving machine-
learning technology, enabling multiple clients to collaboratively train a global model without …

Federated Learning (FL) is an evolving machine learning method in which multiple clients
participate in collaborative learning without sharing their data with each other and the …

Swarm learning (SL) is an emerging promising decentralized machine learning paradigm
and has achieved high performance in clinical applications. By combining edge computing …

The growth of information technology has resulted in a massive escalation of data and the
demand for data exploration, particularly in the machine learning sector. However, machine …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Federated learning (FL) is a popular distributed learning framework that is proposed to
address privacy concerns in traditional machine learning. However, recent research has …

Deep learning models have raised privacy and security concerns due to their reliance on
large datasets on central servers. As the number of Internet of Things (IoT) devices …

Federated learning (FL) is an evolving machine learning technique that allows collaborative
model training without sharing the original data among participants. In real-world scenarios …