This report discusses the opportunities and challenges in federated learning (fl), in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server.
Given the growing interest in the fl domain, this report discusses the opportunities and challenges in federated learning, in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server.
Explainable machine learning (ml) enables human learning from ml, humanappeal of automated model decisions, regulatory compliance, and security audits of machine learning models.
Explainable machine learning has been implemented in numerous open source and commercial packages and explainable ml is also an important, mandatory, or embedded aspect of commercial predictive modeling in industries like financial services.
We discuss how the nature of streaming machine learning problems introduces new real-world challenges (e.g., delayed arrival of labels) and recommend additional metrics to assess streaming machine learning performance.
This paper reviews the entire engineering process of trustworthy machine learning algorithms designed to equip critical systems with advanced analytics and decision functions.
We start from the fundamental principles of machine learning and describe the core elements conditioning its trust, particularly through its design : namely domain specification, data engineering, design of the mlalgorithms, their implementation, evaluation and deployment.