Distributed Machine Learning Systems: Advances, Challenges, and Future Directions

Abstract

Distributed machine learning systems have emerged as a critical component in handling the growing complexity and scale of modern data processing and model training. This paper provides a comprehensive review of the current state of distributed machine learning systems, focusing on their architecture, methodologies, and applications. We explore the advancements in distributed learning algorithms, the challenges associated with scaling and maintaining distributed systems, and potential future directions for research and development in this rapidly evolving field.