Introduction to the Special Issue on Federated Machine Learning

Abstract

The articles in this special section focus on federated machine learning, an emerging research paradigm focusing on solving data-silos challenges in real-world industrial applications. It is a broad discipline that touches many topics, including distributed and collaborative learning, privacy-preserving machine learning, edge computing, and data valuation, etc. Its interdisciplinary nature calls for collaborative efforts from a variety of fields to establish new protocols, frameworks and systems to address unique challenges, and open problems. These articles highlight a selection of high-quality and original works in this new area, including accepted papers to the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019.