Wearable devices and medical sensors revolutionize health monitoring, raising concerns
about data privacy in ML for healthcare. This tutorial explores FL and BC integration, offering …

The performance of intelligent vehicles (IVs) in object detection relies not only on the design
or scale of the CNN model they use but also on how effectively they share their acquired …

Abstract The advent of Federated Learning (FL) highlights the practical necessity for the'right
to be forgotten'for all clients, allowing them to request data deletion from the machine …

The rapid increase in data volume and variety within the field of machine learning
necessitates ethical data utilization and adherence to strict privacy protection standards. Fair …

Group fairness in machine learning is a critical area of research focused on achieving
equitable outcomes across different groups defined by sensitive attributes such as race or …

Federated Learning (FL) has been proven to show biased predictions against certain
demographic groups, such as sex or race. Recent advances in improving fairness in …

Federated learning (FL) enables collaborative model training while preserving data privacy,
making it suitable for decentralized human-centered AI applications. However, a significant …

Federated learning (FL) has gained significant attention in distributed machine learning due
to its ability to protect data privacy while enabling model training across decentralized data …

Federated learning (FL) enables collaborative model training from decentralized data while
preserving privacy. However, biases manifest due to sample selection, population drift …

This research addresses the challenge of community detection in federated learning
environments where data is non-independent and identically distributed across clients. We …