Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Federated learning (FL) refers to a system of training and stabilizing local machine learning
models at the global level by aggregating the learning gradients of the models. It reduces …

AI is remarkably successful and outperforms human experts in certain tasks, even in
complex domains such as medicine. Humans on the other hand are experts at multi-modal …

This chapter focuses on describing how the human‐centered design (HCD) process can be
revisited and expanded in an artificial intelligence (AI) context, proposing a methodological …

Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple
participants collaboratively to train a global model without uploading raw data. Considering …

We explore the integration of domain knowledge graphs into Deep Learning for improved
interpretability and explainability using Graph Neural Networks (GNNs). Specifically, a …

Privacy protection is one of the key concerns of users in recommender system-based
consumer markets. Popular recommendation frameworks such as collaborative filtering (CF) …

Digital pathology is not only one of the most promising fields of diagnostic medicine, but at
the same time a hot topic for fundamental research. Digital pathology is not just the transfer …

This paper describes a new collaborative filtering recommendation algorithm based on
probability matrix factorization. The proposed algorithm decomposes the rating matrix into …

Large-scale software systems are generally deployed on distributed machines. Logs are
usually collected from those machines for comprehensive and accurate system fault …