Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …

Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …

The purpose of federated learning is to enable multiple clients to jointly train a machine
learning model without sharing data. However, the existing methods for training an image …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

Healthcare datasets are becoming larger and more complex, necessitating the development
of accurate and generalizable AI models for medical applications. Unstructured datasets …

Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …

Emerging Distributed AI systems are revolutionizing big data computing and data
processing capabilities with growing economic and societal impact. However, recent studies …

Recently, in healthcare organizations, real-time data have been collected from connected or
implantable sensors, layered protocol stacks, lightweight communication frameworks, and …