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 …

Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …

Federated learning (FL) is a powerful technology that enables collaborative training of
machine learning models without sharing private data among clients. The fundamental …

Federated learning enables collaborative training of a model while kee** the training data
decentralized and private. However, in IoT systems, inherent heterogeneity in processing …

With the booming development of fifth-generation network technology and Internet of Things,
the number of end-user devices (EDs) and diverse applications is surging, resulting in …

Federated Learning (FL) has emerged as a powerful approach that enables collaborative
distributed model training without the need for data sharing. However, FL grapples with …

Federated Learning (FL) is an emerging paradigm for training machine learning models
across decentralized edge devices, ensuring data privacy and reducing computational tasks …

Federated Learning (FL) enables collaborative training of Machine Learning (ML) models
across decentralized clients while preserving data privacy. One of the challenges that FL …

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …

Genome-wide association studies (GWAS) serve as a crucial tool for identifying genetic
factors associated with specific traits. However, ethical constraints prevent the direct …