Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …

State-of-the-art AI models largely lack an understanding of the cause-effect relationship that
governs human understanding of the real world. Consequently, these models do not …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

Federated learning (FL) enables decentralized model training, but the distribution of data
across devices presents significant challenges to global model convergence. Existing …

Federated Learning (FL) has recently emerged as a novel machine learning paradigm
allowing to preserve privacy and to account for the distributed nature of the learning process …

Federated learning is a method for training models in a distributed environment where each
client utilizes its local dataset to train a model and shares it with a server to create a global …

Domain generalization (DG) aims to learn from multiple known source domains a model that
can generalize well to unknown target domains. The existing DG methods usually exploit the …

Objectives The objective was to review current research and practices for using FL to
generate synthetic data and determine the extent to which research has been undertaken …

Assigning repetitive and physically demanding construction tasks to robots can alleviate
human workers' exposure to occupational injuries, which often result in significant downtime …